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A researcher measures the sleep of 376 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 288 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 121 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 257 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 244 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 126 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 291 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 133 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 136 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 266 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 168 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 57 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 89 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 193 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 84 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 154 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 338 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 111 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 95 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 231 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 105 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 75 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 68 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 318 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 292 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 98 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 303 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 129 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 325 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 314 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 175 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 438 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 242 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 319 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 144 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 285 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 300 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 328 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 362 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 150 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 483 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 224 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 149 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 310 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 177 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 267 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 151 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 123 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 254 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 219 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
To learn how much US college students spend on books, a team surveys 671 students at 20 colleges. What is the population?
1. All US college students
2. The 671 students surveyed
3. The 20 colleges
To learn how much US college students spend on books, a team surveys 351 students at 20 colleges. What is the population?
1. All US college students
2. The 351 students surveyed
3. The 20 colleges
To learn how much US college students spend on books, a team surveys 876 students at 20 colleges. What is the population?
1. All US college students
2. The 876 students surveyed
3. The 20 colleges
To learn how much US college students spend on books, a team surveys 464 students at 20 colleges. What is the population?
1. All US college students
2. The 464 students surveyed
3. The 20 colleges
To learn how much US college students spend on books, a team surveys 319 students at 20 colleges. What is the population?
1. All US college students
2. The 319 students surveyed
3. The 20 colleges
To learn how much US college students spend on books, a team surveys 417 students at 20 colleges. What is the population?
1. All US college students
2. The 417 students surveyed
3. The 20 colleges
To learn how much US college students spend on books, a team surveys 858 students at 20 colleges. What is the population?
1. All US college students
2. The 858 students surveyed
3. The 20 colleges
To learn how much US college students spend on books, a team surveys 637 students at 20 colleges. What is the population?
1. All US college students
2. The 637 students surveyed
3. The 20 colleges
To learn how much US college students spend on books, a team surveys 178 students at 20 colleges. What is the population?
1. All US college students
2. The 178 students surveyed
3. The 20 colleges
To learn how much US college students spend on books, a team surveys 262 students at 20 colleges. What is the population?
1. All US college students
2. The 262 students surveyed
3. The 20 colleges
To learn how much US college students spend on books, a team surveys 217 students at 20 colleges. What is the population?
1. All US college students
2. The 217 students surveyed
3. The 20 colleges
To learn how much US college students spend on books, a team surveys 333 students at 20 colleges. What is the population?
1. All US college students
2. The 333 students surveyed
3. The 20 colleges
To learn how much US college students spend on books, a team surveys 230 students at 20 colleges. What is the population?
1. All US college students
2. The 230 students surveyed
3. The 20 colleges
To learn how much US college students spend on books, a team surveys 536 students at 20 colleges. What is the population?
1. All US college students
2. The 536 students surveyed
3. The 20 colleges
To learn how much US college students spend on books, a team surveys 208 students at 20 colleges. What is the population?
1. All US college students
2. The 208 students surveyed
3. The 20 colleges
To learn how much US college students spend on books, a team surveys 639 students at 20 colleges. What is the population?
1. All US college students
2. The 639 students surveyed
3. The 20 colleges
To learn how much US college students spend on books, a team surveys 539 students at 20 colleges. What is the population?
1. All US college students
2. The 539 students surveyed
3. The 20 colleges
To learn how much US college students spend on books, a team surveys 622 students at 20 colleges. What is the population?
1. All US college students
2. The 622 students surveyed
3. The 20 colleges
To learn how much US college students spend on books, a team surveys 580 students at 20 colleges. What is the population?
1. All US college students
2. The 580 students surveyed
3. The 20 colleges
To learn how much US college students spend on books, a team surveys 588 students at 20 colleges. What is the population?
1. All US college students
2. The 588 students surveyed
3. The 20 colleges
To learn how much US college students spend on books, a team surveys 209 students at 20 colleges. What is the population?
1. All US college students
2. The 209 students surveyed
3. The 20 colleges
To learn how much US college students spend on books, a team surveys 461 students at 20 colleges. What is the population?
1. All US college students
2. The 461 students surveyed
3. The 20 colleges
To learn how much US college students spend on books, a team surveys 334 students at 20 colleges. What is the population?
1. All US college students
2. The 334 students surveyed
3. The 20 colleges
To learn how much US college students spend on books, a team surveys 859 students at 20 colleges. What is the population?
1. All US college students
2. The 859 students surveyed
3. The 20 colleges
To learn how much US college students spend on books, a team surveys 282 students at 20 colleges. What is the population?
1. All US college students
2. The 282 students surveyed
3. The 20 colleges
To learn how much US college students spend on books, a team surveys 658 students at 20 colleges. What is the population?
1. All US college students
2. The 658 students surveyed
3. The 20 colleges
To learn how much US college students spend on books, a team surveys 696 students at 20 colleges. What is the population?
1. All US college students
2. The 696 students surveyed
3. The 20 colleges
To learn how much US college students spend on books, a team surveys 556 students at 20 colleges. What is the population?
1. All US college students
2. The 556 students surveyed
3. The 20 colleges
To learn how much US college students spend on books, a team surveys 762 students at 20 colleges. What is the population?
1. All US college students
2. The 762 students surveyed
3. The 20 colleges
To learn how much US college students spend on books, a team surveys 832 students at 20 colleges. What is the population?
1. All US college students
2. The 832 students surveyed
3. The 20 colleges
To learn how much US college students spend on books, a team surveys 355 students at 20 colleges. What is the population?
1. All US college students
2. The 355 students surveyed
3. The 20 colleges
To learn how much US college students spend on books, a team surveys 712 students at 20 colleges. What is the population?
1. All US college students
2. The 712 students surveyed
3. The 20 colleges
To learn how much US college students spend on books, a team surveys 427 students at 20 colleges. What is the population?
1. All US college students
2. The 427 students surveyed
3. The 20 colleges
To learn how much US college students spend on books, a team surveys 833 students at 20 colleges. What is the population?
1. All US college students
2. The 833 students surveyed
3. The 20 colleges
To learn how much US college students spend on books, a team surveys 462 students at 20 colleges. What is the population?
1. All US college students
2. The 462 students surveyed
3. The 20 colleges
To learn how much US college students spend on books, a team surveys 677 students at 20 colleges. What is the population?
1. All US college students
2. The 677 students surveyed
3. The 20 colleges
To learn how much US college students spend on books, a team surveys 104 students at 20 colleges. What is the population?
1. All US college students
2. The 104 students surveyed
3. The 20 colleges
To learn how much US college students spend on books, a team surveys 774 students at 20 colleges. What is the population?
1. All US college students
2. The 774 students surveyed
3. The 20 colleges
To learn how much US college students spend on books, a team surveys 569 students at 20 colleges. What is the population?
1. All US college students
2. The 569 students surveyed
3. The 20 colleges
To learn how much US college students spend on books, a team surveys 519 students at 20 colleges. What is the population?
1. All US college students
2. The 519 students surveyed
3. The 20 colleges
To learn how much US college students spend on books, a team surveys 280 students at 20 colleges. What is the population?
1. All US college students
2. The 280 students surveyed
3. The 20 colleges
To learn how much US college students spend on books, a team surveys 635 students at 20 colleges. What is the population?
1. All US college students
2. The 635 students surveyed
3. The 20 colleges
To learn how much US college students spend on books, a team surveys 493 students at 20 colleges. What is the population?
1. All US college students
2. The 493 students surveyed
3. The 20 colleges
To learn how much US college students spend on books, a team surveys 227 students at 20 colleges. What is the population?
1. All US college students
2. The 227 students surveyed
3. The 20 colleges
To learn how much US college students spend on books, a team surveys 808 students at 20 colleges. What is the population?
1. All US college students
2. The 808 students surveyed
3. The 20 colleges
To learn how much US college students spend on books, a team surveys 188 students at 20 colleges. What is the population?
1. All US college students
2. The 188 students surveyed
3. The 20 colleges
To learn how much US college students spend on books, a team surveys 877 students at 20 colleges. What is the population?
1. All US college students
2. The 877 students surveyed
3. The 20 colleges
To learn how much US college students spend on books, a team surveys 789 students at 20 colleges. What is the population?
1. All US college students
2. The 789 students surveyed
3. The 20 colleges
To learn how much US college students spend on books, a team surveys 669 students at 20 colleges. What is the population?
1. All US college students
2. The 669 students surveyed
3. The 20 colleges
To learn how much US college students spend on books, a team surveys 510 students at 20 colleges. What is the population?
1. All US college students
2. The 510 students surveyed
3. The 20 colleges
Census records show that 52% of all households in a town own a car. What is 52%?
1. A parameter: it describes the whole population
2. A statistic
3. A sample proportion
Census records show that 55% of all households in a town own a car. What is 55%?
1. A parameter: it describes the whole population
2. A statistic
3. A sample proportion
Census records show that 60% of all households in a town own a car. What is 60%?
1. A parameter: it describes the whole population
2. A statistic
3. A sample proportion
Census records show that 40% of all households in a town own a car. What is 40%?
1. A parameter: it describes the whole population
2. A statistic
3. A sample proportion
Census records show that 57% of all households in a town own a car. What is 57%?
1. A parameter: it describes the whole population
2. A statistic
3. A sample proportion
Census records show that 42% of all households in a town own a car. What is 42%?
1. A parameter: it describes the whole population
2. A statistic
3. A sample proportion
Census records show that 49% of all households in a town own a car. What is 49%?
1. A parameter: it describes the whole population
2. A statistic
3. A sample proportion
Census records show that 51% of all households in a town own a car. What is 51%?
1. A parameter: it describes the whole population
2. A statistic
3. A sample proportion
Census records show that 58% of all households in a town own a car. What is 58%?
1. A parameter: it describes the whole population
2. A statistic
3. A sample proportion
Census records show that 50% of all households in a town own a car. What is 50%?
1. A parameter: it describes the whole population
2. A statistic
3. A sample proportion
Census records show that 45% of all households in a town own a car. What is 45%?
1. A parameter: it describes the whole population
2. A statistic
3. A sample proportion
Census records show that 53% of all households in a town own a car. What is 53%?
1. A parameter: it describes the whole population
2. A statistic
3. A sample proportion
Census records show that 56% of all households in a town own a car. What is 56%?
1. A parameter: it describes the whole population
2. A statistic
3. A sample proportion
Census records show that 59% of all households in a town own a car. What is 59%?
1. A parameter: it describes the whole population
2. A statistic
3. A sample proportion
Census records show that 48% of all households in a town own a car. What is 48%?
1. A parameter: it describes the whole population
2. A statistic
3. A sample proportion
Census records show that 47% of all households in a town own a car. What is 47%?
1. A parameter: it describes the whole population
2. A statistic
3. A sample proportion
Census records show that 43% of all households in a town own a car. What is 43%?
1. A parameter: it describes the whole population
2. A statistic
3. A sample proportion
Census records show that 46% of all households in a town own a car. What is 46%?
1. A parameter: it describes the whole population
2. A statistic
3. A sample proportion
Census records show that 54% of all households in a town own a car. What is 54%?
1. A parameter: it describes the whole population
2. A statistic
3. A sample proportion
Census records show that 41% of all households in a town own a car. What is 41%?
1. A parameter: it describes the whole population
2. A statistic
3. A sample proportion
Census records show that 44% of all households in a town own a car. What is 44%?
1. A parameter: it describes the whole population
2. A statistic
3. A sample proportion
A news website asks readers to vote online; 4824 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 4824 responses are representative
A news website asks readers to vote online; 1714 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 1714 responses are representative
A news website asks readers to vote online; 3716 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 3716 responses are representative
A news website asks readers to vote online; 1560 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 1560 responses are representative
A news website asks readers to vote online; 1035 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 1035 responses are representative
A news website asks readers to vote online; 1225 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 1225 responses are representative
A news website asks readers to vote online; 866 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 866 responses are representative
A news website asks readers to vote online; 1442 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 1442 responses are representative
A news website asks readers to vote online; 3042 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 3042 responses are representative
A news website asks readers to vote online; 2939 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 2939 responses are representative
A news website asks readers to vote online; 3780 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 3780 responses are representative
A news website asks readers to vote online; 3381 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 3381 responses are representative
A news website asks readers to vote online; 4312 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 4312 responses are representative
A news website asks readers to vote online; 4089 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 4089 responses are representative
A news website asks readers to vote online; 2087 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 2087 responses are representative
A news website asks readers to vote online; 4745 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 4745 responses are representative
A news website asks readers to vote online; 4130 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 4130 responses are representative
A news website asks readers to vote online; 1359 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 1359 responses are representative
A news website asks readers to vote online; 2941 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 2941 responses are representative
A news website asks readers to vote online; 4407 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 4407 responses are representative
A news website asks readers to vote online; 4025 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 4025 responses are representative
A news website asks readers to vote online; 4086 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 4086 responses are representative
A news website asks readers to vote online; 2631 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 2631 responses are representative
A news website asks readers to vote online; 598 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 598 responses are representative
A news website asks readers to vote online; 602 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 602 responses are representative
A news website asks readers to vote online; 4358 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 4358 responses are representative
A news website asks readers to vote online; 3213 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 3213 responses are representative
A news website asks readers to vote online; 3873 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 3873 responses are representative
A news website asks readers to vote online; 1872 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 1872 responses are representative
A news website asks readers to vote online; 1732 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 1732 responses are representative
A news website asks readers to vote online; 4507 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 4507 responses are representative
A news website asks readers to vote online; 3855 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 3855 responses are representative
A news website asks readers to vote online; 3591 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 3591 responses are representative
A news website asks readers to vote online; 1002 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 1002 responses are representative
A news website asks readers to vote online; 3188 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 3188 responses are representative
A news website asks readers to vote online; 1545 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 1545 responses are representative
A news website asks readers to vote online; 1734 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 1734 responses are representative
A news website asks readers to vote online; 1949 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 1949 responses are representative
A news website asks readers to vote online; 1077 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 1077 responses are representative
A news website asks readers to vote online; 1979 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 1979 responses are representative
A news website asks readers to vote online; 4847 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 4847 responses are representative
A news website asks readers to vote online; 1616 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 1616 responses are representative
A news website asks readers to vote online; 2473 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 2473 responses are representative
A news website asks readers to vote online; 1938 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 1938 responses are representative
A news website asks readers to vote online; 4351 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 4351 responses are representative
A news website asks readers to vote online; 1755 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 1755 responses are representative
A news website asks readers to vote online; 4385 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 4385 responses are representative
A news website asks readers to vote online; 2748 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 2748 responses are representative
A news website asks readers to vote online; 1328 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 1328 responses are representative
A news website asks readers to vote online; 4228 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 4228 responses are representative
A survey of 115 adults is done by calling landline numbers. What kind of bias is most likely?
1. Undercoverage: people without landlines cannot be chosen
2. Response bias from the wording
3. None, if the numbers are chosen at random
A survey of 715 adults is done by calling landline numbers. What kind of bias is most likely?
1. Undercoverage: people without landlines cannot be chosen
2. Response bias from the wording
3. None, if the numbers are chosen at random
A survey of 619 adults is done by calling landline numbers. What kind of bias is most likely?
1. Undercoverage: people without landlines cannot be chosen
2. Response bias from the wording
3. None, if the numbers are chosen at random
A survey of 410 adults is done by calling landline numbers. What kind of bias is most likely?
1. Undercoverage: people without landlines cannot be chosen
2. Response bias from the wording
3. None, if the numbers are chosen at random
A survey of 361 adults is done by calling landline numbers. What kind of bias is most likely?
1. Undercoverage: people without landlines cannot be chosen
2. Response bias from the wording
3. None, if the numbers are chosen at random
A survey of 189 adults is done by calling landline numbers. What kind of bias is most likely?
1. Undercoverage: people without landlines cannot be chosen
2. Response bias from the wording
3. None, if the numbers are chosen at random
A survey of 159 adults is done by calling landline numbers. What kind of bias is most likely?
1. Undercoverage: people without landlines cannot be chosen
2. Response bias from the wording
3. None, if the numbers are chosen at random
A survey of 369 adults is done by calling landline numbers. What kind of bias is most likely?
1. Undercoverage: people without landlines cannot be chosen
2. Response bias from the wording
3. None, if the numbers are chosen at random
A survey of 443 adults is done by calling landline numbers. What kind of bias is most likely?
1. Undercoverage: people without landlines cannot be chosen
2. Response bias from the wording
3. None, if the numbers are chosen at random
A survey of 510 adults is done by calling landline numbers. What kind of bias is most likely?
1. Undercoverage: people without landlines cannot be chosen
2. Response bias from the wording
3. None, if the numbers are chosen at random
A survey of 196 adults is done by calling landline numbers. What kind of bias is most likely?
1. Undercoverage: people without landlines cannot be chosen
2. Response bias from the wording
3. None, if the numbers are chosen at random
A survey of 749 adults is done by calling landline numbers. What kind of bias is most likely?
1. Undercoverage: people without landlines cannot be chosen
2. Response bias from the wording
3. None, if the numbers are chosen at random
A survey of 763 adults is done by calling landline numbers. What kind of bias is most likely?
1. Undercoverage: people without landlines cannot be chosen
2. Response bias from the wording
3. None, if the numbers are chosen at random
A survey of 563 adults is done by calling landline numbers. What kind of bias is most likely?
1. Undercoverage: people without landlines cannot be chosen
2. Response bias from the wording
3. None, if the numbers are chosen at random
A survey of 707 adults is done by calling landline numbers. What kind of bias is most likely?
1. Undercoverage: people without landlines cannot be chosen
2. Response bias from the wording
3. None, if the numbers are chosen at random
A survey of 675 adults is done by calling landline numbers. What kind of bias is most likely?
1. Undercoverage: people without landlines cannot be chosen
2. Response bias from the wording
3. None, if the numbers are chosen at random
A survey of 145 adults is done by calling landline numbers. What kind of bias is most likely?
1. Undercoverage: people without landlines cannot be chosen
2. Response bias from the wording
3. None, if the numbers are chosen at random
A survey of 326 adults is done by calling landline numbers. What kind of bias is most likely?
1. Undercoverage: people without landlines cannot be chosen
2. Response bias from the wording
3. None, if the numbers are chosen at random
A survey of 211 adults is done by calling landline numbers. What kind of bias is most likely?
1. Undercoverage: people without landlines cannot be chosen
2. Response bias from the wording
3. None, if the numbers are chosen at random
A survey of 471 adults is done by calling landline numbers. What kind of bias is most likely?
1. Undercoverage: people without landlines cannot be chosen
2. Response bias from the wording
3. None, if the numbers are chosen at random
A survey of 415 adults is done by calling landline numbers. What kind of bias is most likely?
1. Undercoverage: people without landlines cannot be chosen
2. Response bias from the wording
3. None, if the numbers are chosen at random
A survey of 775 adults is done by calling landline numbers. What kind of bias is most likely?
1. Undercoverage: people without landlines cannot be chosen
2. Response bias from the wording
3. None, if the numbers are chosen at random
A survey of 798 adults is done by calling landline numbers. What kind of bias is most likely?
1. Undercoverage: people without landlines cannot be chosen
2. Response bias from the wording
3. None, if the numbers are chosen at random
A survey of 873 adults is done by calling landline numbers. What kind of bias is most likely?
1. Undercoverage: people without landlines cannot be chosen
2. Response bias from the wording
3. None, if the numbers are chosen at random
A survey of 858 adults is done by calling landline numbers. What kind of bias is most likely?
1. Undercoverage: people without landlines cannot be chosen
2. Response bias from the wording
3. None, if the numbers are chosen at random
A survey of 440 adults is done by calling landline numbers. What kind of bias is most likely?
1. Undercoverage: people without landlines cannot be chosen
2. Response bias from the wording
3. None, if the numbers are chosen at random
A survey of 138 adults is done by calling landline numbers. What kind of bias is most likely?
1. Undercoverage: people without landlines cannot be chosen
2. Response bias from the wording
3. None, if the numbers are chosen at random
A survey of 108 adults is done by calling landline numbers. What kind of bias is most likely?
1. Undercoverage: people without landlines cannot be chosen
2. Response bias from the wording
3. None, if the numbers are chosen at random
A survey of 897 adults is done by calling landline numbers. What kind of bias is most likely?
1. Undercoverage: people without landlines cannot be chosen
2. Response bias from the wording
3. None, if the numbers are chosen at random
A survey of 585 adults is done by calling landline numbers. What kind of bias is most likely?
1. Undercoverage: people without landlines cannot be chosen
2. Response bias from the wording
3. None, if the numbers are chosen at random
A survey of 608 adults is done by calling landline numbers. What kind of bias is most likely?
1. Undercoverage: people without landlines cannot be chosen
2. Response bias from the wording
3. None, if the numbers are chosen at random
A survey of 418 adults is done by calling landline numbers. What kind of bias is most likely?
1. Undercoverage: people without landlines cannot be chosen
2. Response bias from the wording
3. None, if the numbers are chosen at random
A survey of 104 adults is done by calling landline numbers. What kind of bias is most likely?
1. Undercoverage: people without landlines cannot be chosen
2. Response bias from the wording
3. None, if the numbers are chosen at random
A survey of 484 adults is done by calling landline numbers. What kind of bias is most likely?
1. Undercoverage: people without landlines cannot be chosen
2. Response bias from the wording
3. None, if the numbers are chosen at random
A survey of 496 adults is done by calling landline numbers. What kind of bias is most likely?
1. Undercoverage: people without landlines cannot be chosen
2. Response bias from the wording
3. None, if the numbers are chosen at random
A survey of 302 adults is done by calling landline numbers. What kind of bias is most likely?
1. Undercoverage: people without landlines cannot be chosen
2. Response bias from the wording
3. None, if the numbers are chosen at random
A survey of 291 adults is done by calling landline numbers. What kind of bias is most likely?
1. Undercoverage: people without landlines cannot be chosen
2. Response bias from the wording
3. None, if the numbers are chosen at random
A survey of 193 adults is done by calling landline numbers. What kind of bias is most likely?
1. Undercoverage: people without landlines cannot be chosen
2. Response bias from the wording
3. None, if the numbers are chosen at random
A survey of 631 adults is done by calling landline numbers. What kind of bias is most likely?
1. Undercoverage: people without landlines cannot be chosen
2. Response bias from the wording
3. None, if the numbers are chosen at random
A survey of 634 adults is done by calling landline numbers. What kind of bias is most likely?
1. Undercoverage: people without landlines cannot be chosen
2. Response bias from the wording
3. None, if the numbers are chosen at random
A survey of 487 adults is done by calling landline numbers. What kind of bias is most likely?
1. Undercoverage: people without landlines cannot be chosen
2. Response bias from the wording
3. None, if the numbers are chosen at random
A survey of 803 adults is done by calling landline numbers. What kind of bias is most likely?
1. Undercoverage: people without landlines cannot be chosen
2. Response bias from the wording
3. None, if the numbers are chosen at random
A survey of 556 adults is done by calling landline numbers. What kind of bias is most likely?
1. Undercoverage: people without landlines cannot be chosen
2. Response bias from the wording
3. None, if the numbers are chosen at random
A survey of 868 adults is done by calling landline numbers. What kind of bias is most likely?
1. Undercoverage: people without landlines cannot be chosen
2. Response bias from the wording
3. None, if the numbers are chosen at random
A survey of 889 adults is done by calling landline numbers. What kind of bias is most likely?
1. Undercoverage: people without landlines cannot be chosen
2. Response bias from the wording
3. None, if the numbers are chosen at random
A survey of 411 adults is done by calling landline numbers. What kind of bias is most likely?
1. Undercoverage: people without landlines cannot be chosen
2. Response bias from the wording
3. None, if the numbers are chosen at random
A survey of 458 adults is done by calling landline numbers. What kind of bias is most likely?
1. Undercoverage: people without landlines cannot be chosen
2. Response bias from the wording
3. None, if the numbers are chosen at random
A survey of 668 adults is done by calling landline numbers. What kind of bias is most likely?
1. Undercoverage: people without landlines cannot be chosen
2. Response bias from the wording
3. None, if the numbers are chosen at random
A survey of 151 adults is done by calling landline numbers. What kind of bias is most likely?
1. Undercoverage: people without landlines cannot be chosen
2. Response bias from the wording
3. None, if the numbers are chosen at random
A survey of 422 adults is done by calling landline numbers. What kind of bias is most likely?
1. Undercoverage: people without landlines cannot be chosen
2. Response bias from the wording
3. None, if the numbers are chosen at random
A biased survey of 238 people is repeated with 10 times as many people, chosen the same way. What happens to the bias?
1. It stays: a larger sample reduces random variability, not bias
2. It disappears
3. It becomes 10 times smaller
A biased survey of 402 people is repeated with 10 times as many people, chosen the same way. What happens to the bias?
1. It stays: a larger sample reduces random variability, not bias
2. It disappears
3. It becomes 10 times smaller
A biased survey of 520 people is repeated with 10 times as many people, chosen the same way. What happens to the bias?
1. It stays: a larger sample reduces random variability, not bias
2. It disappears
3. It becomes 10 times smaller
A biased survey of 555 people is repeated with 10 times as many people, chosen the same way. What happens to the bias?
1. It stays: a larger sample reduces random variability, not bias
2. It disappears
3. It becomes 10 times smaller
A biased survey of 666 people is repeated with 10 times as many people, chosen the same way. What happens to the bias?
1. It stays: a larger sample reduces random variability, not bias
2. It disappears
3. It becomes 10 times smaller
A biased survey of 583 people is repeated with 10 times as many people, chosen the same way. What happens to the bias?
1. It stays: a larger sample reduces random variability, not bias
2. It disappears
3. It becomes 10 times smaller
A biased survey of 355 people is repeated with 10 times as many people, chosen the same way. What happens to the bias?
1. It stays: a larger sample reduces random variability, not bias
2. It disappears
3. It becomes 10 times smaller
A biased survey of 891 people is repeated with 10 times as many people, chosen the same way. What happens to the bias?
1. It stays: a larger sample reduces random variability, not bias
2. It disappears
3. It becomes 10 times smaller
A biased survey of 718 people is repeated with 10 times as many people, chosen the same way. What happens to the bias?
1. It stays: a larger sample reduces random variability, not bias
2. It disappears
3. It becomes 10 times smaller
A biased survey of 517 people is repeated with 10 times as many people, chosen the same way. What happens to the bias?
1. It stays: a larger sample reduces random variability, not bias
2. It disappears
3. It becomes 10 times smaller
A biased survey of 819 people is repeated with 10 times as many people, chosen the same way. What happens to the bias?
1. It stays: a larger sample reduces random variability, not bias
2. It disappears
3. It becomes 10 times smaller
A biased survey of 307 people is repeated with 10 times as many people, chosen the same way. What happens to the bias?
1. It stays: a larger sample reduces random variability, not bias
2. It disappears
3. It becomes 10 times smaller
A biased survey of 446 people is repeated with 10 times as many people, chosen the same way. What happens to the bias?
1. It stays: a larger sample reduces random variability, not bias
2. It disappears
3. It becomes 10 times smaller
A biased survey of 789 people is repeated with 10 times as many people, chosen the same way. What happens to the bias?
1. It stays: a larger sample reduces random variability, not bias
2. It disappears
3. It becomes 10 times smaller
A biased survey of 392 people is repeated with 10 times as many people, chosen the same way. What happens to the bias?
1. It stays: a larger sample reduces random variability, not bias
2. It disappears
3. It becomes 10 times smaller
A biased survey of 126 people is repeated with 10 times as many people, chosen the same way. What happens to the bias?
1. It stays: a larger sample reduces random variability, not bias
2. It disappears
3. It becomes 10 times smaller
A biased survey of 706 people is repeated with 10 times as many people, chosen the same way. What happens to the bias?
1. It stays: a larger sample reduces random variability, not bias
2. It disappears
3. It becomes 10 times smaller
A biased survey of 653 people is repeated with 10 times as many people, chosen the same way. What happens to the bias?
1. It stays: a larger sample reduces random variability, not bias
2. It disappears
3. It becomes 10 times smaller
A biased survey of 352 people is repeated with 10 times as many people, chosen the same way. What happens to the bias?
1. It stays: a larger sample reduces random variability, not bias
2. It disappears
3. It becomes 10 times smaller
A biased survey of 403 people is repeated with 10 times as many people, chosen the same way. What happens to the bias?
1. It stays: a larger sample reduces random variability, not bias
2. It disappears
3. It becomes 10 times smaller
A biased survey of 300 people is repeated with 10 times as many people, chosen the same way. What happens to the bias?
1. It stays: a larger sample reduces random variability, not bias
2. It disappears
3. It becomes 10 times smaller
A biased survey of 462 people is repeated with 10 times as many people, chosen the same way. What happens to the bias?
1. It stays: a larger sample reduces random variability, not bias
2. It disappears
3. It becomes 10 times smaller
A biased survey of 708 people is repeated with 10 times as many people, chosen the same way. What happens to the bias?
1. It stays: a larger sample reduces random variability, not bias
2. It disappears
3. It becomes 10 times smaller
A biased survey of 253 people is repeated with 10 times as many people, chosen the same way. What happens to the bias?
1. It stays: a larger sample reduces random variability, not bias
2. It disappears
3. It becomes 10 times smaller
A biased survey of 862 people is repeated with 10 times as many people, chosen the same way. What happens to the bias?
1. It stays: a larger sample reduces random variability, not bias
2. It disappears
3. It becomes 10 times smaller
A biased survey of 205 people is repeated with 10 times as many people, chosen the same way. What happens to the bias?
1. It stays: a larger sample reduces random variability, not bias
2. It disappears
3. It becomes 10 times smaller
A biased survey of 609 people is repeated with 10 times as many people, chosen the same way. What happens to the bias?
1. It stays: a larger sample reduces random variability, not bias
2. It disappears
3. It becomes 10 times smaller
A biased survey of 846 people is repeated with 10 times as many people, chosen the same way. What happens to the bias?
1. It stays: a larger sample reduces random variability, not bias
2. It disappears
3. It becomes 10 times smaller
A biased survey of 867 people is repeated with 10 times as many people, chosen the same way. What happens to the bias?
1. It stays: a larger sample reduces random variability, not bias
2. It disappears
3. It becomes 10 times smaller
A biased survey of 288 people is repeated with 10 times as many people, chosen the same way. What happens to the bias?
1. It stays: a larger sample reduces random variability, not bias
2. It disappears
3. It becomes 10 times smaller
A biased survey of 351 people is repeated with 10 times as many people, chosen the same way. What happens to the bias?
1. It stays: a larger sample reduces random variability, not bias
2. It disappears
3. It becomes 10 times smaller
A biased survey of 496 people is repeated with 10 times as many people, chosen the same way. What happens to the bias?
1. It stays: a larger sample reduces random variability, not bias
2. It disappears
3. It becomes 10 times smaller
A biased survey of 223 people is repeated with 10 times as many people, chosen the same way. What happens to the bias?
1. It stays: a larger sample reduces random variability, not bias
2. It disappears
3. It becomes 10 times smaller
A biased survey of 692 people is repeated with 10 times as many people, chosen the same way. What happens to the bias?
1. It stays: a larger sample reduces random variability, not bias
2. It disappears
3. It becomes 10 times smaller
A biased survey of 705 people is repeated with 10 times as many people, chosen the same way. What happens to the bias?
1. It stays: a larger sample reduces random variability, not bias
2. It disappears
3. It becomes 10 times smaller
A biased survey of 473 people is repeated with 10 times as many people, chosen the same way. What happens to the bias?
1. It stays: a larger sample reduces random variability, not bias
2. It disappears
3. It becomes 10 times smaller
A biased survey of 740 people is repeated with 10 times as many people, chosen the same way. What happens to the bias?
1. It stays: a larger sample reduces random variability, not bias
2. It disappears
3. It becomes 10 times smaller
A biased survey of 600 people is repeated with 10 times as many people, chosen the same way. What happens to the bias?
1. It stays: a larger sample reduces random variability, not bias
2. It disappears
3. It becomes 10 times smaller
A biased survey of 357 people is repeated with 10 times as many people, chosen the same way. What happens to the bias?
1. It stays: a larger sample reduces random variability, not bias
2. It disappears
3. It becomes 10 times smaller
A biased survey of 188 people is repeated with 10 times as many people, chosen the same way. What happens to the bias?
1. It stays: a larger sample reduces random variability, not bias
2. It disappears
3. It becomes 10 times smaller
A biased survey of 700 people is repeated with 10 times as many people, chosen the same way. What happens to the bias?
1. It stays: a larger sample reduces random variability, not bias
2. It disappears
3. It becomes 10 times smaller
A biased survey of 289 people is repeated with 10 times as many people, chosen the same way. What happens to the bias?
1. It stays: a larger sample reduces random variability, not bias
2. It disappears
3. It becomes 10 times smaller
A biased survey of 756 people is repeated with 10 times as many people, chosen the same way. What happens to the bias?
1. It stays: a larger sample reduces random variability, not bias
2. It disappears
3. It becomes 10 times smaller
A biased survey of 640 people is repeated with 10 times as many people, chosen the same way. What happens to the bias?
1. It stays: a larger sample reduces random variability, not bias
2. It disappears
3. It becomes 10 times smaller
A biased survey of 396 people is repeated with 10 times as many people, chosen the same way. What happens to the bias?
1. It stays: a larger sample reduces random variability, not bias
2. It disappears
3. It becomes 10 times smaller
A biased survey of 835 people is repeated with 10 times as many people, chosen the same way. What happens to the bias?
1. It stays: a larger sample reduces random variability, not bias
2. It disappears
3. It becomes 10 times smaller
A biased survey of 864 people is repeated with 10 times as many people, chosen the same way. What happens to the bias?
1. It stays: a larger sample reduces random variability, not bias
2. It disappears
3. It becomes 10 times smaller
A biased survey of 833 people is repeated with 10 times as many people, chosen the same way. What happens to the bias?
1. It stays: a larger sample reduces random variability, not bias
2. It disappears
3. It becomes 10 times smaller
A biased survey of 721 people is repeated with 10 times as many people, chosen the same way. What happens to the bias?
1. It stays: a larger sample reduces random variability, not bias
2. It disappears
3. It becomes 10 times smaller
A biased survey of 136 people is repeated with 10 times as many people, chosen the same way. What happens to the bias?
1. It stays: a larger sample reduces random variability, not bias
2. It disappears
3. It becomes 10 times smaller
To test H₀: p₁ = p₂, group 1 had 21 successes out of 82 and group 2 had 22 out of 102. Find the pooled proportion.
24 students take a test before and after a course. Which procedure compares the scores?
1. A paired t test on the 24 differences (after − before)
2. A two-sample t test treating the groups as independent
3. A two-proportion z test
10 students take a test before and after a course. Which procedure compares the scores?
1. A paired t test on the 10 differences (after − before)
2. A two-sample t test treating the groups as independent
3. A two-proportion z test
38 students take a test before and after a course. Which procedure compares the scores?
1. A paired t test on the 38 differences (after − before)
2. A two-sample t test treating the groups as independent
3. A two-proportion z test
13 students take a test before and after a course. Which procedure compares the scores?
1. A paired t test on the 13 differences (after − before)
2. A two-sample t test treating the groups as independent
3. A two-proportion z test
17 students take a test before and after a course. Which procedure compares the scores?
1. A paired t test on the 17 differences (after − before)
2. A two-sample t test treating the groups as independent
3. A two-proportion z test
31 students take a test before and after a course. Which procedure compares the scores?
1. A paired t test on the 31 differences (after − before)
2. A two-sample t test treating the groups as independent
3. A two-proportion z test
14 students take a test before and after a course. Which procedure compares the scores?
1. A paired t test on the 14 differences (after − before)
2. A two-sample t test treating the groups as independent
3. A two-proportion z test
18 students take a test before and after a course. Which procedure compares the scores?
1. A paired t test on the 18 differences (after − before)
2. A two-sample t test treating the groups as independent
3. A two-proportion z test
39 students take a test before and after a course. Which procedure compares the scores?
1. A paired t test on the 39 differences (after − before)
2. A two-sample t test treating the groups as independent
3. A two-proportion z test
20 students take a test before and after a course. Which procedure compares the scores?
1. A paired t test on the 20 differences (after − before)
2. A two-sample t test treating the groups as independent
3. A two-proportion z test
25 students take a test before and after a course. Which procedure compares the scores?
1. A paired t test on the 25 differences (after − before)
2. A two-sample t test treating the groups as independent
3. A two-proportion z test
37 students take a test before and after a course. Which procedure compares the scores?
1. A paired t test on the 37 differences (after − before)
2. A two-sample t test treating the groups as independent
3. A two-proportion z test
34 students take a test before and after a course. Which procedure compares the scores?
1. A paired t test on the 34 differences (after − before)
2. A two-sample t test treating the groups as independent
3. A two-proportion z test
22 students take a test before and after a course. Which procedure compares the scores?
1. A paired t test on the 22 differences (after − before)
2. A two-sample t test treating the groups as independent
3. A two-proportion z test
16 students take a test before and after a course. Which procedure compares the scores?
1. A paired t test on the 16 differences (after − before)
2. A two-sample t test treating the groups as independent
3. A two-proportion z test
27 students take a test before and after a course. Which procedure compares the scores?
1. A paired t test on the 27 differences (after − before)
2. A two-sample t test treating the groups as independent
3. A two-proportion z test
11 students take a test before and after a course. Which procedure compares the scores?
1. A paired t test on the 11 differences (after − before)
2. A two-sample t test treating the groups as independent
3. A two-proportion z test
29 students take a test before and after a course. Which procedure compares the scores?
1. A paired t test on the 29 differences (after − before)
2. A two-sample t test treating the groups as independent
3. A two-proportion z test
23 students take a test before and after a course. Which procedure compares the scores?
1. A paired t test on the 23 differences (after − before)
2. A two-sample t test treating the groups as independent
3. A two-proportion z test
12 students take a test before and after a course. Which procedure compares the scores?
1. A paired t test on the 12 differences (after − before)
2. A two-sample t test treating the groups as independent
3. A two-proportion z test
35 students take a test before and after a course. Which procedure compares the scores?
1. A paired t test on the 35 differences (after − before)
2. A two-sample t test treating the groups as independent
3. A two-proportion z test
33 students take a test before and after a course. Which procedure compares the scores?
1. A paired t test on the 33 differences (after − before)
2. A two-sample t test treating the groups as independent
3. A two-proportion z test
19 students take a test before and after a course. Which procedure compares the scores?
1. A paired t test on the 19 differences (after − before)
2. A two-sample t test treating the groups as independent
3. A two-proportion z test
28 students take a test before and after a course. Which procedure compares the scores?
1. A paired t test on the 28 differences (after − before)
2. A two-sample t test treating the groups as independent
3. A two-proportion z test
30 students take a test before and after a course. Which procedure compares the scores?
1. A paired t test on the 30 differences (after − before)
2. A two-sample t test treating the groups as independent
3. A two-proportion z test
32 students take a test before and after a course. Which procedure compares the scores?
1. A paired t test on the 32 differences (after − before)
2. A two-sample t test treating the groups as independent
3. A two-proportion z test
21 students take a test before and after a course. Which procedure compares the scores?
1. A paired t test on the 21 differences (after − before)
2. A two-sample t test treating the groups as independent
3. A two-proportion z test
40 students take a test before and after a course. Which procedure compares the scores?
1. A paired t test on the 40 differences (after − before)
2. A two-sample t test treating the groups as independent
3. A two-proportion z test
26 students take a test before and after a course. Which procedure compares the scores?
1. A paired t test on the 26 differences (after − before)
2. A two-sample t test treating the groups as independent
3. A two-proportion z test
15 students take a test before and after a course. Which procedure compares the scores?
1. A paired t test on the 15 differences (after − before)
2. A two-sample t test treating the groups as independent
3. A two-proportion z test
36 students take a test before and after a course. Which procedure compares the scores?
1. A paired t test on the 36 differences (after − before)
2. A two-sample t test treating the groups as independent
3. A two-proportion z test
Testing H₀: p₁ = p₂ with n₁ = n₂ = 100, the pooled proportion is 3/10. Find the standard error of p̂₁ − p̂₂.
Test scores have mean 75 and standard deviation 3. Find the z-score of a score of 52.
Heights are approximately normal with mean 76 and standard deviation 4. By the 68–95–99.7 rule, what percent are between 72 and 84?
Mean 71, standard deviation 8. Which value has z-score $-3$ ?
Researchers randomly assign 66 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 166 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 46 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 133 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 173 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 144 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 40 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 45 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 195 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 189 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 141 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 196 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 112 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 78 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 96 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 169 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 145 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 152 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 129 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 65 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 97 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 70 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 109 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 111 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 51 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 49 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 197 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 188 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 161 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 179 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 155 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 125 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 74 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 177 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 59 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 147 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 124 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 41 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 48 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 93 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 192 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 110 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 200 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 178 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 153 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 84 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 180 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 151 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 194 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 163 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Children with bigger shoes read better (16% higher scores). What is the most likely explanation?
1. A confounding variable: age affects both shoe size and reading
2. Bigger feet improve reading
3. It must be pure chance
Children with bigger shoes read better (13% higher scores). What is the most likely explanation?
1. A confounding variable: age affects both shoe size and reading
2. Bigger feet improve reading
3. It must be pure chance
Children with bigger shoes read better (30% higher scores). What is the most likely explanation?
1. A confounding variable: age affects both shoe size and reading
2. Bigger feet improve reading
3. It must be pure chance
Children with bigger shoes read better (37% higher scores). What is the most likely explanation?
1. A confounding variable: age affects both shoe size and reading
2. Bigger feet improve reading
3. It must be pure chance
Children with bigger shoes read better (36% higher scores). What is the most likely explanation?
1. A confounding variable: age affects both shoe size and reading
2. Bigger feet improve reading
3. It must be pure chance
Children with bigger shoes read better (35% higher scores). What is the most likely explanation?
1. A confounding variable: age affects both shoe size and reading
2. Bigger feet improve reading
3. It must be pure chance
Children with bigger shoes read better (20% higher scores). What is the most likely explanation?
1. A confounding variable: age affects both shoe size and reading
2. Bigger feet improve reading
3. It must be pure chance
Children with bigger shoes read better (23% higher scores). What is the most likely explanation?
1. A confounding variable: age affects both shoe size and reading
2. Bigger feet improve reading
3. It must be pure chance
Children with bigger shoes read better (15% higher scores). What is the most likely explanation?
1. A confounding variable: age affects both shoe size and reading
2. Bigger feet improve reading
3. It must be pure chance
Children with bigger shoes read better (25% higher scores). What is the most likely explanation?
1. A confounding variable: age affects both shoe size and reading
2. Bigger feet improve reading
3. It must be pure chance
Children with bigger shoes read better (27% higher scores). What is the most likely explanation?
1. A confounding variable: age affects both shoe size and reading
2. Bigger feet improve reading
3. It must be pure chance
Children with bigger shoes read better (39% higher scores). What is the most likely explanation?
1. A confounding variable: age affects both shoe size and reading
2. Bigger feet improve reading
3. It must be pure chance
Children with bigger shoes read better (10% higher scores). What is the most likely explanation?
1. A confounding variable: age affects both shoe size and reading
2. Bigger feet improve reading
3. It must be pure chance
Children with bigger shoes read better (18% higher scores). What is the most likely explanation?
1. A confounding variable: age affects both shoe size and reading
2. Bigger feet improve reading
3. It must be pure chance
Children with bigger shoes read better (22% higher scores). What is the most likely explanation?
1. A confounding variable: age affects both shoe size and reading
2. Bigger feet improve reading
3. It must be pure chance
Children with bigger shoes read better (29% higher scores). What is the most likely explanation?
1. A confounding variable: age affects both shoe size and reading
2. Bigger feet improve reading
3. It must be pure chance
Children with bigger shoes read better (17% higher scores). What is the most likely explanation?
1. A confounding variable: age affects both shoe size and reading
2. Bigger feet improve reading
3. It must be pure chance
Children with bigger shoes read better (38% higher scores). What is the most likely explanation?
1. A confounding variable: age affects both shoe size and reading
2. Bigger feet improve reading
3. It must be pure chance
Children with bigger shoes read better (34% higher scores). What is the most likely explanation?
1. A confounding variable: age affects both shoe size and reading
2. Bigger feet improve reading
3. It must be pure chance
Children with bigger shoes read better (19% higher scores). What is the most likely explanation?
1. A confounding variable: age affects both shoe size and reading
2. Bigger feet improve reading
3. It must be pure chance
Children with bigger shoes read better (12% higher scores). What is the most likely explanation?
1. A confounding variable: age affects both shoe size and reading
2. Bigger feet improve reading
3. It must be pure chance
Children with bigger shoes read better (26% higher scores). What is the most likely explanation?
1. A confounding variable: age affects both shoe size and reading
2. Bigger feet improve reading
3. It must be pure chance
Children with bigger shoes read better (33% higher scores). What is the most likely explanation?
1. A confounding variable: age affects both shoe size and reading
2. Bigger feet improve reading
3. It must be pure chance
Children with bigger shoes read better (31% higher scores). What is the most likely explanation?
1. A confounding variable: age affects both shoe size and reading
2. Bigger feet improve reading
3. It must be pure chance
Children with bigger shoes read better (14% higher scores). What is the most likely explanation?
1. A confounding variable: age affects both shoe size and reading
2. Bigger feet improve reading
3. It must be pure chance
Children with bigger shoes read better (24% higher scores). What is the most likely explanation?
1. A confounding variable: age affects both shoe size and reading
2. Bigger feet improve reading
3. It must be pure chance
Children with bigger shoes read better (11% higher scores). What is the most likely explanation?
1. A confounding variable: age affects both shoe size and reading
2. Bigger feet improve reading
3. It must be pure chance
Children with bigger shoes read better (28% higher scores). What is the most likely explanation?
1. A confounding variable: age affects both shoe size and reading
2. Bigger feet improve reading
3. It must be pure chance
Children with bigger shoes read better (21% higher scores). What is the most likely explanation?
1. A confounding variable: age affects both shoe size and reading
2. Bigger feet improve reading
3. It must be pure chance
Children with bigger shoes read better (32% higher scores). What is the most likely explanation?
1. A confounding variable: age affects both shoe size and reading
2. Bigger feet improve reading
3. It must be pure chance
Children with bigger shoes read better (40% higher scores). What is the most likely explanation?
1. A confounding variable: age affects both shoe size and reading
2. Bigger feet improve reading
3. It must be pure chance
An observational study of 3416 adults finds that those who eat breakfast weigh less. Does this show that eating breakfast causes lower weight?
1. No: other differences between the groups could explain it
2. Yes, because the sample is large
3. Yes, if the association is strong
An observational study of 5700 adults finds that those who eat breakfast weigh less. Does this show that eating breakfast causes lower weight?
1. No: other differences between the groups could explain it
2. Yes, because the sample is large
3. Yes, if the association is strong
An observational study of 8655 adults finds that those who eat breakfast weigh less. Does this show that eating breakfast causes lower weight?
1. No: other differences between the groups could explain it
2. Yes, because the sample is large
3. Yes, if the association is strong
An observational study of 1601 adults finds that those who eat breakfast weigh less. Does this show that eating breakfast causes lower weight?
1. No: other differences between the groups could explain it
2. Yes, because the sample is large
3. Yes, if the association is strong
An observational study of 5074 adults finds that those who eat breakfast weigh less. Does this show that eating breakfast causes lower weight?
1. No: other differences between the groups could explain it
2. Yes, because the sample is large
3. Yes, if the association is strong
An observational study of 7764 adults finds that those who eat breakfast weigh less. Does this show that eating breakfast causes lower weight?
1. No: other differences between the groups could explain it
2. Yes, because the sample is large
3. Yes, if the association is strong
An observational study of 2314 adults finds that those who eat breakfast weigh less. Does this show that eating breakfast causes lower weight?
1. No: other differences between the groups could explain it
2. Yes, because the sample is large
3. Yes, if the association is strong
An observational study of 3314 adults finds that those who eat breakfast weigh less. Does this show that eating breakfast causes lower weight?
1. No: other differences between the groups could explain it
2. Yes, because the sample is large
3. Yes, if the association is strong
An observational study of 2139 adults finds that those who eat breakfast weigh less. Does this show that eating breakfast causes lower weight?
1. No: other differences between the groups could explain it
2. Yes, because the sample is large
3. Yes, if the association is strong
An observational study of 6746 adults finds that those who eat breakfast weigh less. Does this show that eating breakfast causes lower weight?
1. No: other differences between the groups could explain it
2. Yes, because the sample is large
3. Yes, if the association is strong
An observational study of 4943 adults finds that those who eat breakfast weigh less. Does this show that eating breakfast causes lower weight?
1. No: other differences between the groups could explain it
2. Yes, because the sample is large
3. Yes, if the association is strong
An observational study of 863 adults finds that those who eat breakfast weigh less. Does this show that eating breakfast causes lower weight?
1. No: other differences between the groups could explain it
2. Yes, because the sample is large
3. Yes, if the association is strong
An observational study of 1943 adults finds that those who eat breakfast weigh less. Does this show that eating breakfast causes lower weight?
1. No: other differences between the groups could explain it
2. Yes, because the sample is large
3. Yes, if the association is strong
An observational study of 3273 adults finds that those who eat breakfast weigh less. Does this show that eating breakfast causes lower weight?
1. No: other differences between the groups could explain it
2. Yes, because the sample is large
3. Yes, if the association is strong
An observational study of 7388 adults finds that those who eat breakfast weigh less. Does this show that eating breakfast causes lower weight?
1. No: other differences between the groups could explain it
2. Yes, because the sample is large
3. Yes, if the association is strong
An observational study of 8847 adults finds that those who eat breakfast weigh less. Does this show that eating breakfast causes lower weight?
1. No: other differences between the groups could explain it
2. Yes, because the sample is large
3. Yes, if the association is strong
An observational study of 8745 adults finds that those who eat breakfast weigh less. Does this show that eating breakfast causes lower weight?
1. No: other differences between the groups could explain it
2. Yes, because the sample is large
3. Yes, if the association is strong
An observational study of 745 adults finds that those who eat breakfast weigh less. Does this show that eating breakfast causes lower weight?
1. No: other differences between the groups could explain it
2. Yes, because the sample is large
3. Yes, if the association is strong
An observational study of 2788 adults finds that those who eat breakfast weigh less. Does this show that eating breakfast causes lower weight?
1. No: other differences between the groups could explain it
2. Yes, because the sample is large
3. Yes, if the association is strong
An observational study of 4529 adults finds that those who eat breakfast weigh less. Does this show that eating breakfast causes lower weight?
1. No: other differences between the groups could explain it
2. Yes, because the sample is large
3. Yes, if the association is strong
An observational study of 5749 adults finds that those who eat breakfast weigh less. Does this show that eating breakfast causes lower weight?
1. No: other differences between the groups could explain it
2. Yes, because the sample is large
3. Yes, if the association is strong
An observational study of 1756 adults finds that those who eat breakfast weigh less. Does this show that eating breakfast causes lower weight?
1. No: other differences between the groups could explain it
2. Yes, because the sample is large
3. Yes, if the association is strong
An observational study of 5236 adults finds that those who eat breakfast weigh less. Does this show that eating breakfast causes lower weight?
1. No: other differences between the groups could explain it
2. Yes, because the sample is large
3. Yes, if the association is strong
An observational study of 3831 adults finds that those who eat breakfast weigh less. Does this show that eating breakfast causes lower weight?
1. No: other differences between the groups could explain it
2. Yes, because the sample is large
3. Yes, if the association is strong
An observational study of 3387 adults finds that those who eat breakfast weigh less. Does this show that eating breakfast causes lower weight?
1. No: other differences between the groups could explain it
2. Yes, because the sample is large
3. Yes, if the association is strong
An observational study of 632 adults finds that those who eat breakfast weigh less. Does this show that eating breakfast causes lower weight?
1. No: other differences between the groups could explain it
2. Yes, because the sample is large
3. Yes, if the association is strong
An observational study of 576 adults finds that those who eat breakfast weigh less. Does this show that eating breakfast causes lower weight?
1. No: other differences between the groups could explain it
2. Yes, because the sample is large
3. Yes, if the association is strong
An observational study of 4932 adults finds that those who eat breakfast weigh less. Does this show that eating breakfast causes lower weight?
1. No: other differences between the groups could explain it
2. Yes, because the sample is large
3. Yes, if the association is strong
An observational study of 8595 adults finds that those who eat breakfast weigh less. Does this show that eating breakfast causes lower weight?
1. No: other differences between the groups could explain it
2. Yes, because the sample is large
3. Yes, if the association is strong
An observational study of 3079 adults finds that those who eat breakfast weigh less. Does this show that eating breakfast causes lower weight?
1. No: other differences between the groups could explain it
2. Yes, because the sample is large
3. Yes, if the association is strong
An observational study of 4804 adults finds that those who eat breakfast weigh less. Does this show that eating breakfast causes lower weight?
1. No: other differences between the groups could explain it
2. Yes, because the sample is large
3. Yes, if the association is strong
An observational study of 4762 adults finds that those who eat breakfast weigh less. Does this show that eating breakfast causes lower weight?
1. No: other differences between the groups could explain it
2. Yes, because the sample is large
3. Yes, if the association is strong
An observational study of 2487 adults finds that those who eat breakfast weigh less. Does this show that eating breakfast causes lower weight?
1. No: other differences between the groups could explain it
2. Yes, because the sample is large
3. Yes, if the association is strong
An observational study of 8033 adults finds that those who eat breakfast weigh less. Does this show that eating breakfast causes lower weight?
1. No: other differences between the groups could explain it
2. Yes, because the sample is large
3. Yes, if the association is strong
An observational study of 614 adults finds that those who eat breakfast weigh less. Does this show that eating breakfast causes lower weight?
1. No: other differences between the groups could explain it
2. Yes, because the sample is large
3. Yes, if the association is strong
An observational study of 798 adults finds that those who eat breakfast weigh less. Does this show that eating breakfast causes lower weight?
1. No: other differences between the groups could explain it
2. Yes, because the sample is large
3. Yes, if the association is strong
An observational study of 8965 adults finds that those who eat breakfast weigh less. Does this show that eating breakfast causes lower weight?
1. No: other differences between the groups could explain it
2. Yes, because the sample is large
3. Yes, if the association is strong
An observational study of 3192 adults finds that those who eat breakfast weigh less. Does this show that eating breakfast causes lower weight?
1. No: other differences between the groups could explain it
2. Yes, because the sample is large
3. Yes, if the association is strong
An observational study of 2599 adults finds that those who eat breakfast weigh less. Does this show that eating breakfast causes lower weight?
1. No: other differences between the groups could explain it
2. Yes, because the sample is large
3. Yes, if the association is strong
An observational study of 1147 adults finds that those who eat breakfast weigh less. Does this show that eating breakfast causes lower weight?
1. No: other differences between the groups could explain it
2. Yes, because the sample is large
3. Yes, if the association is strong
An observational study of 1724 adults finds that those who eat breakfast weigh less. Does this show that eating breakfast causes lower weight?
1. No: other differences between the groups could explain it
2. Yes, because the sample is large
3. Yes, if the association is strong
An observational study of 1811 adults finds that those who eat breakfast weigh less. Does this show that eating breakfast causes lower weight?
1. No: other differences between the groups could explain it
2. Yes, because the sample is large
3. Yes, if the association is strong
An observational study of 4824 adults finds that those who eat breakfast weigh less. Does this show that eating breakfast causes lower weight?
1. No: other differences between the groups could explain it
2. Yes, because the sample is large
3. Yes, if the association is strong
An observational study of 7222 adults finds that those who eat breakfast weigh less. Does this show that eating breakfast causes lower weight?
1. No: other differences between the groups could explain it
2. Yes, because the sample is large
3. Yes, if the association is strong
An observational study of 1744 adults finds that those who eat breakfast weigh less. Does this show that eating breakfast causes lower weight?
1. No: other differences between the groups could explain it
2. Yes, because the sample is large
3. Yes, if the association is strong
An observational study of 7929 adults finds that those who eat breakfast weigh less. Does this show that eating breakfast causes lower weight?
1. No: other differences between the groups could explain it
2. Yes, because the sample is large
3. Yes, if the association is strong
An observational study of 3510 adults finds that those who eat breakfast weigh less. Does this show that eating breakfast causes lower weight?
1. No: other differences between the groups could explain it
2. Yes, because the sample is large
3. Yes, if the association is strong
An observational study of 7028 adults finds that those who eat breakfast weigh less. Does this show that eating breakfast causes lower weight?
1. No: other differences between the groups could explain it
2. Yes, because the sample is large
3. Yes, if the association is strong
An observational study of 2504 adults finds that those who eat breakfast weigh less. Does this show that eating breakfast causes lower weight?
1. No: other differences between the groups could explain it
2. Yes, because the sample is large
3. Yes, if the association is strong
An observational study of 5206 adults finds that those who eat breakfast weigh less. Does this show that eating breakfast causes lower weight?
1. No: other differences between the groups could explain it
2. Yes, because the sample is large
3. Yes, if the association is strong
Why are 196 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 185 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 189 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 46 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 188 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 47 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 65 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 101 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 66 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 102 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 111 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 85 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 54 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 84 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 75 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 122 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 129 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 61 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 57 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 92 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 132 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 55 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 178 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 96 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 41 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 184 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 182 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 118 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 179 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 154 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 169 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 43 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 97 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 143 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 183 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 76 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 48 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 79 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 151 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 60 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 147 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 78 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 83 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 139 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 155 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 69 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 103 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 115 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 187 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 198 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
A test of two diets on 115 adults first splits them into men and women, then randomizes diets within each group. What is this design?
1. A randomized block design, blocking by sex
2. A stratified random sample
3. A completely randomized design
A test of two diets on 45 adults first splits them into men and women, then randomizes diets within each group. What is this design?
1. A randomized block design, blocking by sex
2. A stratified random sample
3. A completely randomized design
A test of two diets on 100 adults first splits them into men and women, then randomizes diets within each group. What is this design?
1. A randomized block design, blocking by sex
2. A stratified random sample
3. A completely randomized design
A test of two diets on 94 adults first splits them into men and women, then randomizes diets within each group. What is this design?
1. A randomized block design, blocking by sex
2. A stratified random sample
3. A completely randomized design
A test of two diets on 68 adults first splits them into men and women, then randomizes diets within each group. What is this design?
1. A randomized block design, blocking by sex
2. A stratified random sample
3. A completely randomized design
A test of two diets on 88 adults first splits them into men and women, then randomizes diets within each group. What is this design?
1. A randomized block design, blocking by sex
2. A stratified random sample
3. A completely randomized design
A test of two diets on 72 adults first splits them into men and women, then randomizes diets within each group. What is this design?
1. A randomized block design, blocking by sex
2. A stratified random sample
3. A completely randomized design
A test of two diets on 128 adults first splits them into men and women, then randomizes diets within each group. What is this design?
1. A randomized block design, blocking by sex
2. A stratified random sample
3. A completely randomized design
A test of two diets on 179 adults first splits them into men and women, then randomizes diets within each group. What is this design?
1. A randomized block design, blocking by sex
2. A stratified random sample
3. A completely randomized design
A test of two diets on 90 adults first splits them into men and women, then randomizes diets within each group. What is this design?
1. A randomized block design, blocking by sex
2. A stratified random sample
3. A completely randomized design
A test of two diets on 141 adults first splits them into men and women, then randomizes diets within each group. What is this design?
1. A randomized block design, blocking by sex
2. A stratified random sample
3. A completely randomized design
A test of two diets on 122 adults first splits them into men and women, then randomizes diets within each group. What is this design?
1. A randomized block design, blocking by sex
2. A stratified random sample
3. A completely randomized design
A test of two diets on 97 adults first splits them into men and women, then randomizes diets within each group. What is this design?
1. A randomized block design, blocking by sex
2. A stratified random sample
3. A completely randomized design
A test of two diets on 142 adults first splits them into men and women, then randomizes diets within each group. What is this design?
1. A randomized block design, blocking by sex
2. A stratified random sample
3. A completely randomized design
A test of two diets on 78 adults first splits them into men and women, then randomizes diets within each group. What is this design?
1. A randomized block design, blocking by sex
2. A stratified random sample
3. A completely randomized design
A test of two diets on 139 adults first splits them into men and women, then randomizes diets within each group. What is this design?
1. A randomized block design, blocking by sex
2. A stratified random sample
3. A completely randomized design
A test of two diets on 84 adults first splits them into men and women, then randomizes diets within each group. What is this design?
1. A randomized block design, blocking by sex
2. A stratified random sample
3. A completely randomized design
A test of two diets on 193 adults first splits them into men and women, then randomizes diets within each group. What is this design?
1. A randomized block design, blocking by sex
2. A stratified random sample
3. A completely randomized design
A test of two diets on 149 adults first splits them into men and women, then randomizes diets within each group. What is this design?
1. A randomized block design, blocking by sex
2. A stratified random sample
3. A completely randomized design
A test of two diets on 163 adults first splits them into men and women, then randomizes diets within each group. What is this design?
1. A randomized block design, blocking by sex
2. A stratified random sample
3. A completely randomized design
A test of two diets on 170 adults first splits them into men and women, then randomizes diets within each group. What is this design?
1. A randomized block design, blocking by sex
2. A stratified random sample
3. A completely randomized design
A test of two diets on 79 adults first splits them into men and women, then randomizes diets within each group. What is this design?
1. A randomized block design, blocking by sex
2. A stratified random sample
3. A completely randomized design
A test of two diets on 73 adults first splits them into men and women, then randomizes diets within each group. What is this design?
1. A randomized block design, blocking by sex
2. A stratified random sample
3. A completely randomized design
A test of two diets on 106 adults first splits them into men and women, then randomizes diets within each group. What is this design?
1. A randomized block design, blocking by sex
2. A stratified random sample
3. A completely randomized design
A test of two diets on 105 adults first splits them into men and women, then randomizes diets within each group. What is this design?
1. A randomized block design, blocking by sex
2. A stratified random sample
3. A completely randomized design
A test of two diets on 157 adults first splits them into men and women, then randomizes diets within each group. What is this design?
1. A randomized block design, blocking by sex
2. A stratified random sample
3. A completely randomized design
A test of two diets on 75 adults first splits them into men and women, then randomizes diets within each group. What is this design?
1. A randomized block design, blocking by sex
2. A stratified random sample
3. A completely randomized design
A test of two diets on 155 adults first splits them into men and women, then randomizes diets within each group. What is this design?
1. A randomized block design, blocking by sex
2. A stratified random sample
3. A completely randomized design
A test of two diets on 191 adults first splits them into men and women, then randomizes diets within each group. What is this design?
1. A randomized block design, blocking by sex
2. A stratified random sample
3. A completely randomized design
A test of two diets on 200 adults first splits them into men and women, then randomizes diets within each group. What is this design?
1. A randomized block design, blocking by sex
2. A stratified random sample
3. A completely randomized design
A test of two diets on 110 adults first splits them into men and women, then randomizes diets within each group. What is this design?
1. A randomized block design, blocking by sex
2. A stratified random sample
3. A completely randomized design
A test of two diets on 70 adults first splits them into men and women, then randomizes diets within each group. What is this design?
1. A randomized block design, blocking by sex
2. A stratified random sample
3. A completely randomized design
A test of two diets on 159 adults first splits them into men and women, then randomizes diets within each group. What is this design?
1. A randomized block design, blocking by sex
2. A stratified random sample
3. A completely randomized design
A test of two diets on 188 adults first splits them into men and women, then randomizes diets within each group. What is this design?
1. A randomized block design, blocking by sex
2. A stratified random sample
3. A completely randomized design
A test of two diets on 156 adults first splits them into men and women, then randomizes diets within each group. What is this design?
1. A randomized block design, blocking by sex
2. A stratified random sample
3. A completely randomized design
A test of two diets on 129 adults first splits them into men and women, then randomizes diets within each group. What is this design?
1. A randomized block design, blocking by sex
2. A stratified random sample
3. A completely randomized design
A test of two diets on 53 adults first splits them into men and women, then randomizes diets within each group. What is this design?
1. A randomized block design, blocking by sex
2. A stratified random sample
3. A completely randomized design
A test of two diets on 87 adults first splits them into men and women, then randomizes diets within each group. What is this design?
1. A randomized block design, blocking by sex
2. A stratified random sample
3. A completely randomized design
A test of two diets on 185 adults first splits them into men and women, then randomizes diets within each group. What is this design?
1. A randomized block design, blocking by sex
2. A stratified random sample
3. A completely randomized design
A test of two diets on 41 adults first splits them into men and women, then randomizes diets within each group. What is this design?
1. A randomized block design, blocking by sex
2. A stratified random sample
3. A completely randomized design
A test of two diets on 40 adults first splits them into men and women, then randomizes diets within each group. What is this design?
1. A randomized block design, blocking by sex
2. A stratified random sample
3. A completely randomized design
A test of two diets on 150 adults first splits them into men and women, then randomizes diets within each group. What is this design?
1. A randomized block design, blocking by sex
2. A stratified random sample
3. A completely randomized design
A test of two diets on 116 adults first splits them into men and women, then randomizes diets within each group. What is this design?
1. A randomized block design, blocking by sex
2. A stratified random sample
3. A completely randomized design
A test of two diets on 121 adults first splits them into men and women, then randomizes diets within each group. What is this design?
1. A randomized block design, blocking by sex
2. A stratified random sample
3. A completely randomized design
A test of two diets on 176 adults first splits them into men and women, then randomizes diets within each group. What is this design?
1. A randomized block design, blocking by sex
2. A stratified random sample
3. A completely randomized design
A test of two diets on 147 adults first splits them into men and women, then randomizes diets within each group. What is this design?
1. A randomized block design, blocking by sex
2. A stratified random sample
3. A completely randomized design
A test of two diets on 140 adults first splits them into men and women, then randomizes diets within each group. What is this design?
1. A randomized block design, blocking by sex
2. A stratified random sample
3. A completely randomized design
A test of two diets on 168 adults first splits them into men and women, then randomizes diets within each group. What is this design?
1. A randomized block design, blocking by sex
2. A stratified random sample
3. A completely randomized design
A test of two diets on 93 adults first splits them into men and women, then randomizes diets within each group. What is this design?
1. A randomized block design, blocking by sex
2. A stratified random sample
3. A completely randomized design
A test of two diets on 160 adults first splits them into men and women, then randomizes diets within each group. What is this design?
1. A randomized block design, blocking by sex
2. A stratified random sample
3. A completely randomized design
An experiment tests 2 fertilizer types at 5 watering levels, with every combination used. How many treatments are there?
Many random samples of size 161 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 224 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 490 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 260 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 439 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 410 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 315 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 242 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 409 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 473 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 423 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 230 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 173 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 198 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 58 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 467 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 96 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 195 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 81 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 334 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 91 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 124 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 367 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 487 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 238 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 351 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 113 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 189 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 391 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 382 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 319 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 52 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 220 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 51 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 337 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 316 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 393 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 452 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 102 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 62 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 208 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 95 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 450 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 443 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 75 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 477 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 286 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 374 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 428 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 66 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Compared with samples of size 73, samples of size 292 give sample means that…
1. vary less around the population mean
2. vary more
3. are more biased
Compared with samples of size 72, samples of size 288 give sample means that…
1. vary less around the population mean
2. vary more
3. are more biased
Compared with samples of size 32, samples of size 128 give sample means that…
1. vary less around the population mean
2. vary more
3. are more biased
Compared with samples of size 41, samples of size 164 give sample means that…
1. vary less around the population mean
2. vary more
3. are more biased
Compared with samples of size 127, samples of size 508 give sample means that…
1. vary less around the population mean
2. vary more
3. are more biased
Compared with samples of size 193, samples of size 772 give sample means that…
1. vary less around the population mean
2. vary more
3. are more biased
Compared with samples of size 25, samples of size 100 give sample means that…
1. vary less around the population mean
2. vary more
3. are more biased
Compared with samples of size 80, samples of size 320 give sample means that…
1. vary less around the population mean
2. vary more
3. are more biased
Compared with samples of size 197, samples of size 788 give sample means that…
1. vary less around the population mean
2. vary more
3. are more biased
Compared with samples of size 97, samples of size 388 give sample means that…
1. vary less around the population mean
2. vary more
3. are more biased
Compared with samples of size 66, samples of size 264 give sample means that…
1. vary less around the population mean
2. vary more
3. are more biased
Compared with samples of size 151, samples of size 604 give sample means that…
1. vary less around the population mean
2. vary more
3. are more biased
Compared with samples of size 113, samples of size 452 give sample means that…
1. vary less around the population mean
2. vary more
3. are more biased
Compared with samples of size 84, samples of size 336 give sample means that…
1. vary less around the population mean
2. vary more
3. are more biased
Compared with samples of size 192, samples of size 768 give sample means that…
1. vary less around the population mean
2. vary more
3. are more biased
Compared with samples of size 104, samples of size 416 give sample means that…
1. vary less around the population mean
2. vary more
3. are more biased
Compared with samples of size 107, samples of size 428 give sample means that…
1. vary less around the population mean
2. vary more
3. are more biased
Compared with samples of size 147, samples of size 588 give sample means that…
1. vary less around the population mean
2. vary more
3. are more biased
Compared with samples of size 155, samples of size 620 give sample means that…
1. vary less around the population mean
2. vary more
3. are more biased
Compared with samples of size 116, samples of size 464 give sample means that…
1. vary less around the population mean
2. vary more
3. are more biased
Compared with samples of size 67, samples of size 268 give sample means that…
1. vary less around the population mean
2. vary more
3. are more biased
Compared with samples of size 60, samples of size 240 give sample means that…
1. vary less around the population mean
2. vary more
3. are more biased
Compared with samples of size 137, samples of size 548 give sample means that…
1. vary less around the population mean
2. vary more
3. are more biased
Compared with samples of size 55, samples of size 220 give sample means that…
1. vary less around the population mean
2. vary more
3. are more biased
Compared with samples of size 23, samples of size 92 give sample means that…
1. vary less around the population mean
2. vary more
3. are more biased
Compared with samples of size 65, samples of size 260 give sample means that…
1. vary less around the population mean
2. vary more
3. are more biased
Compared with samples of size 77, samples of size 308 give sample means that…
1. vary less around the population mean
2. vary more
3. are more biased
Compared with samples of size 45, samples of size 180 give sample means that…
1. vary less around the population mean
2. vary more
3. are more biased
Compared with samples of size 93, samples of size 372 give sample means that…
1. vary less around the population mean
2. vary more
3. are more biased
Compared with samples of size 74, samples of size 296 give sample means that…
1. vary less around the population mean
2. vary more
3. are more biased
Compared with samples of size 46, samples of size 184 give sample means that…
1. vary less around the population mean
2. vary more
3. are more biased
Compared with samples of size 159, samples of size 636 give sample means that…
1. vary less around the population mean
2. vary more
3. are more biased
Compared with samples of size 30, samples of size 120 give sample means that…
1. vary less around the population mean
2. vary more
3. are more biased
Compared with samples of size 21, samples of size 84 give sample means that…
1. vary less around the population mean
2. vary more
3. are more biased
Compared with samples of size 191, samples of size 764 give sample means that…
1. vary less around the population mean
2. vary more
3. are more biased
Compared with samples of size 182, samples of size 728 give sample means that…
1. vary less around the population mean
2. vary more
3. are more biased
Compared with samples of size 123, samples of size 492 give sample means that…
1. vary less around the population mean
2. vary more
3. are more biased
Compared with samples of size 153, samples of size 612 give sample means that…
1. vary less around the population mean
2. vary more
3. are more biased
Compared with samples of size 114, samples of size 456 give sample means that…
1. vary less around the population mean
2. vary more
3. are more biased
Compared with samples of size 90, samples of size 360 give sample means that…
1. vary less around the population mean
2. vary more
3. are more biased
Compared with samples of size 117, samples of size 468 give sample means that…
1. vary less around the population mean
2. vary more
3. are more biased
Compared with samples of size 198, samples of size 792 give sample means that…
1. vary less around the population mean
2. vary more
3. are more biased
Compared with samples of size 63, samples of size 252 give sample means that…
1. vary less around the population mean
2. vary more
3. are more biased
Compared with samples of size 102, samples of size 408 give sample means that…
1. vary less around the population mean
2. vary more
3. are more biased
Compared with samples of size 98, samples of size 392 give sample means that…
1. vary less around the population mean
2. vary more
3. are more biased
Compared with samples of size 82, samples of size 328 give sample means that…
1. vary less around the population mean
2. vary more
3. are more biased
Compared with samples of size 89, samples of size 356 give sample means that…
1. vary less around the population mean
2. vary more
3. are more biased
Compared with samples of size 119, samples of size 476 give sample means that…
1. vary less around the population mean
2. vary more
3. are more biased
Compared with samples of size 174, samples of size 696 give sample means that…
1. vary less around the population mean
2. vary more
3. are more biased
Compared with samples of size 190, samples of size 760 give sample means that…
1. vary less around the population mean
2. vary more
3. are more biased
What is the sampling distribution of the sample mean for samples of size 25?
1. The distribution of x̄ over all possible samples of size 25
2. The distribution of the 25 values in one sample
3. The distribution of the population
What is the sampling distribution of the sample mean for samples of size 23?
1. The distribution of x̄ over all possible samples of size 23
2. The distribution of the 23 values in one sample
3. The distribution of the population
What is the sampling distribution of the sample mean for samples of size 46?
1. The distribution of x̄ over all possible samples of size 46
2. The distribution of the 46 values in one sample
3. The distribution of the population
What is the sampling distribution of the sample mean for samples of size 40?
1. The distribution of x̄ over all possible samples of size 40
2. The distribution of the 40 values in one sample
3. The distribution of the population
What is the sampling distribution of the sample mean for samples of size 38?
1. The distribution of x̄ over all possible samples of size 38
2. The distribution of the 38 values in one sample
3. The distribution of the population
What is the sampling distribution of the sample mean for samples of size 86?
1. The distribution of x̄ over all possible samples of size 86
2. The distribution of the 86 values in one sample
3. The distribution of the population
What is the sampling distribution of the sample mean for samples of size 152?
1. The distribution of x̄ over all possible samples of size 152
2. The distribution of the 152 values in one sample
3. The distribution of the population
What is the sampling distribution of the sample mean for samples of size 72?
1. The distribution of x̄ over all possible samples of size 72
2. The distribution of the 72 values in one sample
3. The distribution of the population
What is the sampling distribution of the sample mean for samples of size 159?
1. The distribution of x̄ over all possible samples of size 159
2. The distribution of the 159 values in one sample
3. The distribution of the population
What is the sampling distribution of the sample mean for samples of size 98?
1. The distribution of x̄ over all possible samples of size 98
2. The distribution of the 98 values in one sample
3. The distribution of the population
What is the sampling distribution of the sample mean for samples of size 91?
1. The distribution of x̄ over all possible samples of size 91
2. The distribution of the 91 values in one sample
3. The distribution of the population
What is the sampling distribution of the sample mean for samples of size 59?
1. The distribution of x̄ over all possible samples of size 59
2. The distribution of the 59 values in one sample
3. The distribution of the population
What is the sampling distribution of the sample mean for samples of size 22?
1. The distribution of x̄ over all possible samples of size 22
2. The distribution of the 22 values in one sample
3. The distribution of the population
What is the sampling distribution of the sample mean for samples of size 80?
1. The distribution of x̄ over all possible samples of size 80
2. The distribution of the 80 values in one sample
3. The distribution of the population
What is the sampling distribution of the sample mean for samples of size 162?
1. The distribution of x̄ over all possible samples of size 162
2. The distribution of the 162 values in one sample
3. The distribution of the population
What is the sampling distribution of the sample mean for samples of size 55?
1. The distribution of x̄ over all possible samples of size 55
2. The distribution of the 55 values in one sample
3. The distribution of the population
What is the sampling distribution of the sample mean for samples of size 37?
1. The distribution of x̄ over all possible samples of size 37
2. The distribution of the 37 values in one sample
3. The distribution of the population
What is the sampling distribution of the sample mean for samples of size 56?
1. The distribution of x̄ over all possible samples of size 56
2. The distribution of the 56 values in one sample
3. The distribution of the population
What is the sampling distribution of the sample mean for samples of size 160?
1. The distribution of x̄ over all possible samples of size 160
2. The distribution of the 160 values in one sample
3. The distribution of the population
What is the sampling distribution of the sample mean for samples of size 130?
1. The distribution of x̄ over all possible samples of size 130
2. The distribution of the 130 values in one sample
3. The distribution of the population
What is the sampling distribution of the sample mean for samples of size 67?
1. The distribution of x̄ over all possible samples of size 67
2. The distribution of the 67 values in one sample
3. The distribution of the population
What is the sampling distribution of the sample mean for samples of size 28?
1. The distribution of x̄ over all possible samples of size 28
2. The distribution of the 28 values in one sample
3. The distribution of the population
What is the sampling distribution of the sample mean for samples of size 173?
1. The distribution of x̄ over all possible samples of size 173
2. The distribution of the 173 values in one sample
3. The distribution of the population
What is the sampling distribution of the sample mean for samples of size 69?
1. The distribution of x̄ over all possible samples of size 69
2. The distribution of the 69 values in one sample
3. The distribution of the population
What is the sampling distribution of the sample mean for samples of size 153?
1. The distribution of x̄ over all possible samples of size 153
2. The distribution of the 153 values in one sample
3. The distribution of the population
What is the sampling distribution of the sample mean for samples of size 199?
1. The distribution of x̄ over all possible samples of size 199
2. The distribution of the 199 values in one sample
3. The distribution of the population
What is the sampling distribution of the sample mean for samples of size 106?
1. The distribution of x̄ over all possible samples of size 106
2. The distribution of the 106 values in one sample
3. The distribution of the population
What is the sampling distribution of the sample mean for samples of size 104?
1. The distribution of x̄ over all possible samples of size 104
2. The distribution of the 104 values in one sample
3. The distribution of the population
What is the sampling distribution of the sample mean for samples of size 184?
1. The distribution of x̄ over all possible samples of size 184
2. The distribution of the 184 values in one sample
3. The distribution of the population
What is the sampling distribution of the sample mean for samples of size 133?
1. The distribution of x̄ over all possible samples of size 133
2. The distribution of the 133 values in one sample
3. The distribution of the population
What is the sampling distribution of the sample mean for samples of size 21?
1. The distribution of x̄ over all possible samples of size 21
2. The distribution of the 21 values in one sample
3. The distribution of the population
What is the sampling distribution of the sample mean for samples of size 93?
1. The distribution of x̄ over all possible samples of size 93
2. The distribution of the 93 values in one sample
3. The distribution of the population
What is the sampling distribution of the sample mean for samples of size 167?
1. The distribution of x̄ over all possible samples of size 167
2. The distribution of the 167 values in one sample
3. The distribution of the population
What is the sampling distribution of the sample mean for samples of size 97?
1. The distribution of x̄ over all possible samples of size 97
2. The distribution of the 97 values in one sample
3. The distribution of the population
What is the sampling distribution of the sample mean for samples of size 48?
1. The distribution of x̄ over all possible samples of size 48
2. The distribution of the 48 values in one sample
3. The distribution of the population
What is the sampling distribution of the sample mean for samples of size 61?
1. The distribution of x̄ over all possible samples of size 61
2. The distribution of the 61 values in one sample
3. The distribution of the population
What is the sampling distribution of the sample mean for samples of size 32?
1. The distribution of x̄ over all possible samples of size 32
2. The distribution of the 32 values in one sample
3. The distribution of the population
What is the sampling distribution of the sample mean for samples of size 166?
1. The distribution of x̄ over all possible samples of size 166
2. The distribution of the 166 values in one sample
3. The distribution of the population
What is the sampling distribution of the sample mean for samples of size 176?
1. The distribution of x̄ over all possible samples of size 176
2. The distribution of the 176 values in one sample
3. The distribution of the population
What is the sampling distribution of the sample mean for samples of size 62?
1. The distribution of x̄ over all possible samples of size 62
2. The distribution of the 62 values in one sample
3. The distribution of the population
What is the sampling distribution of the sample mean for samples of size 195?
1. The distribution of x̄ over all possible samples of size 195
2. The distribution of the 195 values in one sample
3. The distribution of the population
What is the sampling distribution of the sample mean for samples of size 95?
1. The distribution of x̄ over all possible samples of size 95
2. The distribution of the 95 values in one sample
3. The distribution of the population
What is the sampling distribution of the sample mean for samples of size 121?
1. The distribution of x̄ over all possible samples of size 121
2. The distribution of the 121 values in one sample
3. The distribution of the population
What is the sampling distribution of the sample mean for samples of size 123?
1. The distribution of x̄ over all possible samples of size 123
2. The distribution of the 123 values in one sample
3. The distribution of the population
What is the sampling distribution of the sample mean for samples of size 83?
1. The distribution of x̄ over all possible samples of size 83
2. The distribution of the 83 values in one sample
3. The distribution of the population
What is the sampling distribution of the sample mean for samples of size 45?
1. The distribution of x̄ over all possible samples of size 45
2. The distribution of the 45 values in one sample
3. The distribution of the population
What is the sampling distribution of the sample mean for samples of size 124?
1. The distribution of x̄ over all possible samples of size 124
2. The distribution of the 124 values in one sample
3. The distribution of the population
What is the sampling distribution of the sample mean for samples of size 113?
1. The distribution of x̄ over all possible samples of size 113
2. The distribution of the 113 values in one sample
3. The distribution of the population
What is the sampling distribution of the sample mean for samples of size 35?
1. The distribution of x̄ over all possible samples of size 35
2. The distribution of the 35 values in one sample
3. The distribution of the population
What is the sampling distribution of the sample mean for samples of size 65?
1. The distribution of x̄ over all possible samples of size 65
2. The distribution of the 65 values in one sample
3. The distribution of the population
A 95% confidence interval for a proportion is (39/100, 49/100). Find the margin of error.
A confidence interval for a mean is (41, 55). Find the point estimate.
A 95% interval from a sample of 591 is too wide. Which change makes it narrower?
1. A larger sample size
2. A higher confidence level, such as 99%
3. Nothing: the width is fixed by the population
A 95% interval from a sample of 890 is too wide. Which change makes it narrower?
1. A larger sample size
2. A higher confidence level, such as 99%
3. Nothing: the width is fixed by the population
A 95% interval from a sample of 583 is too wide. Which change makes it narrower?
1. A larger sample size
2. A higher confidence level, such as 99%
3. Nothing: the width is fixed by the population
A 95% interval from a sample of 819 is too wide. Which change makes it narrower?
1. A larger sample size
2. A higher confidence level, such as 99%
3. Nothing: the width is fixed by the population
A 95% interval from a sample of 508 is too wide. Which change makes it narrower?
1. A larger sample size
2. A higher confidence level, such as 99%
3. Nothing: the width is fixed by the population
A 95% interval from a sample of 266 is too wide. Which change makes it narrower?
1. A larger sample size
2. A higher confidence level, such as 99%
3. Nothing: the width is fixed by the population
A 95% interval from a sample of 427 is too wide. Which change makes it narrower?
1. A larger sample size
2. A higher confidence level, such as 99%
3. Nothing: the width is fixed by the population
A 95% interval from a sample of 482 is too wide. Which change makes it narrower?
1. A larger sample size
2. A higher confidence level, such as 99%
3. Nothing: the width is fixed by the population
A 95% interval from a sample of 108 is too wide. Which change makes it narrower?
1. A larger sample size
2. A higher confidence level, such as 99%
3. Nothing: the width is fixed by the population
A 95% interval from a sample of 642 is too wide. Which change makes it narrower?
1. A larger sample size
2. A higher confidence level, such as 99%
3. Nothing: the width is fixed by the population
A 95% interval from a sample of 670 is too wide. Which change makes it narrower?
1. A larger sample size
2. A higher confidence level, such as 99%
3. Nothing: the width is fixed by the population
A 95% interval from a sample of 745 is too wide. Which change makes it narrower?
1. A larger sample size
2. A higher confidence level, such as 99%
3. Nothing: the width is fixed by the population
A 95% interval from a sample of 320 is too wide. Which change makes it narrower?
1. A larger sample size
2. A higher confidence level, such as 99%
3. Nothing: the width is fixed by the population
A 95% interval from a sample of 141 is too wide. Which change makes it narrower?
1. A larger sample size
2. A higher confidence level, such as 99%
3. Nothing: the width is fixed by the population
A 95% interval from a sample of 172 is too wide. Which change makes it narrower?
1. A larger sample size
2. A higher confidence level, such as 99%
3. Nothing: the width is fixed by the population
A 95% interval from a sample of 294 is too wide. Which change makes it narrower?
1. A larger sample size
2. A higher confidence level, such as 99%
3. Nothing: the width is fixed by the population
A 95% interval from a sample of 681 is too wide. Which change makes it narrower?
1. A larger sample size
2. A higher confidence level, such as 99%
3. Nothing: the width is fixed by the population
A 95% interval from a sample of 157 is too wide. Which change makes it narrower?
1. A larger sample size
2. A higher confidence level, such as 99%
3. Nothing: the width is fixed by the population
A 95% interval from a sample of 369 is too wide. Which change makes it narrower?
1. A larger sample size
2. A higher confidence level, such as 99%
3. Nothing: the width is fixed by the population
A 95% interval from a sample of 220 is too wide. Which change makes it narrower?
1. A larger sample size
2. A higher confidence level, such as 99%
3. Nothing: the width is fixed by the population
A 95% interval from a sample of 726 is too wide. Which change makes it narrower?
1. A larger sample size
2. A higher confidence level, such as 99%
3. Nothing: the width is fixed by the population
A 95% interval from a sample of 276 is too wide. Which change makes it narrower?
1. A larger sample size
2. A higher confidence level, such as 99%
3. Nothing: the width is fixed by the population
A 95% interval from a sample of 156 is too wide. Which change makes it narrower?
1. A larger sample size
2. A higher confidence level, such as 99%
3. Nothing: the width is fixed by the population
A 95% interval from a sample of 414 is too wide. Which change makes it narrower?
1. A larger sample size
2. A higher confidence level, such as 99%
3. Nothing: the width is fixed by the population
A 95% interval from a sample of 398 is too wide. Which change makes it narrower?
1. A larger sample size
2. A higher confidence level, such as 99%
3. Nothing: the width is fixed by the population
A 95% interval from a sample of 236 is too wide. Which change makes it narrower?
1. A larger sample size
2. A higher confidence level, such as 99%
3. Nothing: the width is fixed by the population
A 95% interval from a sample of 399 is too wide. Which change makes it narrower?
1. A larger sample size
2. A higher confidence level, such as 99%
3. Nothing: the width is fixed by the population
A 95% interval from a sample of 311 is too wide. Which change makes it narrower?
1. A larger sample size
2. A higher confidence level, such as 99%
3. Nothing: the width is fixed by the population
A 95% interval from a sample of 898 is too wide. Which change makes it narrower?
1. A larger sample size
2. A higher confidence level, such as 99%
3. Nothing: the width is fixed by the population
A 95% interval from a sample of 247 is too wide. Which change makes it narrower?
1. A larger sample size
2. A higher confidence level, such as 99%
3. Nothing: the width is fixed by the population
A 95% interval from a sample of 595 is too wide. Which change makes it narrower?
1. A larger sample size
2. A higher confidence level, such as 99%
3. Nothing: the width is fixed by the population
A 95% interval from a sample of 719 is too wide. Which change makes it narrower?
1. A larger sample size
2. A higher confidence level, such as 99%
3. Nothing: the width is fixed by the population
A 95% interval from a sample of 411 is too wide. Which change makes it narrower?
1. A larger sample size
2. A higher confidence level, such as 99%
3. Nothing: the width is fixed by the population
A 95% interval from a sample of 684 is too wide. Which change makes it narrower?
1. A larger sample size
2. A higher confidence level, such as 99%
3. Nothing: the width is fixed by the population
A 95% interval from a sample of 589 is too wide. Which change makes it narrower?
1. A larger sample size
2. A higher confidence level, such as 99%
3. Nothing: the width is fixed by the population
A 95% interval from a sample of 503 is too wide. Which change makes it narrower?
1. A larger sample size
2. A higher confidence level, such as 99%
3. Nothing: the width is fixed by the population
A 95% interval from a sample of 758 is too wide. Which change makes it narrower?
1. A larger sample size
2. A higher confidence level, such as 99%
3. Nothing: the width is fixed by the population
A 95% interval from a sample of 848 is too wide. Which change makes it narrower?
1. A larger sample size
2. A higher confidence level, such as 99%
3. Nothing: the width is fixed by the population
A 95% interval from a sample of 349 is too wide. Which change makes it narrower?
1. A larger sample size
2. A higher confidence level, such as 99%
3. Nothing: the width is fixed by the population
A 95% interval from a sample of 459 is too wide. Which change makes it narrower?
1. A larger sample size
2. A higher confidence level, such as 99%
3. Nothing: the width is fixed by the population
A 95% interval from a sample of 393 is too wide. Which change makes it narrower?
1. A larger sample size
2. A higher confidence level, such as 99%
3. Nothing: the width is fixed by the population
A 95% interval from a sample of 844 is too wide. Which change makes it narrower?
1. A larger sample size
2. A higher confidence level, such as 99%
3. Nothing: the width is fixed by the population
A 95% interval from a sample of 662 is too wide. Which change makes it narrower?
1. A larger sample size
2. A higher confidence level, such as 99%
3. Nothing: the width is fixed by the population
A 95% interval from a sample of 205 is too wide. Which change makes it narrower?
1. A larger sample size
2. A higher confidence level, such as 99%
3. Nothing: the width is fixed by the population
A 95% interval from a sample of 545 is too wide. Which change makes it narrower?
1. A larger sample size
2. A higher confidence level, such as 99%
3. Nothing: the width is fixed by the population
A 95% interval from a sample of 520 is too wide. Which change makes it narrower?
1. A larger sample size
2. A higher confidence level, such as 99%
3. Nothing: the width is fixed by the population
A 95% interval from a sample of 534 is too wide. Which change makes it narrower?
1. A larger sample size
2. A higher confidence level, such as 99%
3. Nothing: the width is fixed by the population
A 95% interval from a sample of 143 is too wide. Which change makes it narrower?
1. A larger sample size
2. A higher confidence level, such as 99%
3. Nothing: the width is fixed by the population
A 95% interval from a sample of 739 is too wide. Which change makes it narrower?
1. A larger sample size
2. A higher confidence level, such as 99%
3. Nothing: the width is fixed by the population
A 95% interval from a sample of 693 is too wide. Which change makes it narrower?
1. A larger sample size
2. A higher confidence level, such as 99%
3. Nothing: the width is fixed by the population
In a random sample of 400, p̂ = 5/10. Using z* = 1.96, find the margin of error of a 95% interval for p.
In a random sample of 100, p̂ = 3/10. Using z* = 2, find the upper end of the approximate 95% interval for p.
A 95% confidence interval for the proportion of voters who support a plan is (0.48, 0.52). Which interpretation is right?
1. We are 95% confident that the interval from 0.48 to 0.52 captures the true proportion of supporters
2. 95% of the sample supports the plan
3. There is a 95% probability that p is between 0.48 and 0.52
A 95% confidence interval for the proportion of voters who support a plan is (0.31, 0.53). Which interpretation is right?
1. We are 95% confident that the interval from 0.31 to 0.53 captures the true proportion of supporters
2. 95% of the sample supports the plan
3. There is a 95% probability that p is between 0.31 and 0.53
A 95% confidence interval for the proportion of voters who support a plan is (0.44, 0.55). Which interpretation is right?
1. We are 95% confident that the interval from 0.44 to 0.55 captures the true proportion of supporters
2. 95% of the sample supports the plan
3. There is a 95% probability that p is between 0.44 and 0.55
A 95% confidence interval for the proportion of voters who support a plan is (0.41, 0.57). Which interpretation is right?
1. We are 95% confident that the interval from 0.41 to 0.57 captures the true proportion of supporters
2. 95% of the sample supports the plan
3. There is a 95% probability that p is between 0.41 and 0.57
A 95% confidence interval for the proportion of voters who support a plan is (0.50, 0.54). Which interpretation is right?
1. We are 95% confident that the interval from 0.50 to 0.54 captures the true proportion of supporters
2. 95% of the sample supports the plan
3. There is a 95% probability that p is between 0.50 and 0.54
A 95% confidence interval for the proportion of voters who support a plan is (0.31, 0.54). Which interpretation is right?
1. We are 95% confident that the interval from 0.31 to 0.54 captures the true proportion of supporters
2. 95% of the sample supports the plan
3. There is a 95% probability that p is between 0.31 and 0.54
A 95% confidence interval for the proportion of voters who support a plan is (0.40, 0.54). Which interpretation is right?
1. We are 95% confident that the interval from 0.40 to 0.54 captures the true proportion of supporters
2. 95% of the sample supports the plan
3. There is a 95% probability that p is between 0.40 and 0.54
A 95% confidence interval for the proportion of voters who support a plan is (0.43, 0.56). Which interpretation is right?
1. We are 95% confident that the interval from 0.43 to 0.56 captures the true proportion of supporters
2. 95% of the sample supports the plan
3. There is a 95% probability that p is between 0.43 and 0.56
A 95% confidence interval for the proportion of voters who support a plan is (0.33, 0.53). Which interpretation is right?
1. We are 95% confident that the interval from 0.33 to 0.53 captures the true proportion of supporters
2. 95% of the sample supports the plan
3. There is a 95% probability that p is between 0.33 and 0.53
A 95% confidence interval for the proportion of voters who support a plan is (0.35, 0.60). Which interpretation is right?
1. We are 95% confident that the interval from 0.35 to 0.60 captures the true proportion of supporters
2. 95% of the sample supports the plan
3. There is a 95% probability that p is between 0.35 and 0.60
A 95% confidence interval for the proportion of voters who support a plan is (0.37, 0.54). Which interpretation is right?
1. We are 95% confident that the interval from 0.37 to 0.54 captures the true proportion of supporters
2. 95% of the sample supports the plan
3. There is a 95% probability that p is between 0.37 and 0.54
A 95% confidence interval for the proportion of voters who support a plan is (0.50, 0.55). Which interpretation is right?
1. We are 95% confident that the interval from 0.50 to 0.55 captures the true proportion of supporters
2. 95% of the sample supports the plan
3. There is a 95% probability that p is between 0.50 and 0.55
A 95% confidence interval for the proportion of voters who support a plan is (0.41, 0.60). Which interpretation is right?
1. We are 95% confident that the interval from 0.41 to 0.60 captures the true proportion of supporters
2. 95% of the sample supports the plan
3. There is a 95% probability that p is between 0.41 and 0.60
A 95% confidence interval for the proportion of voters who support a plan is (0.44, 0.57). Which interpretation is right?
1. We are 95% confident that the interval from 0.44 to 0.57 captures the true proportion of supporters
2. 95% of the sample supports the plan
3. There is a 95% probability that p is between 0.44 and 0.57
A 95% confidence interval for the proportion of voters who support a plan is (0.50, 0.51). Which interpretation is right?
1. We are 95% confident that the interval from 0.50 to 0.51 captures the true proportion of supporters
2. 95% of the sample supports the plan
3. There is a 95% probability that p is between 0.50 and 0.51
A 95% confidence interval for the proportion of voters who support a plan is (0.31, 0.56). Which interpretation is right?
1. We are 95% confident that the interval from 0.31 to 0.56 captures the true proportion of supporters
2. 95% of the sample supports the plan
3. There is a 95% probability that p is between 0.31 and 0.56
A 95% confidence interval for the proportion of voters who support a plan is (0.38, 0.51). Which interpretation is right?
1. We are 95% confident that the interval from 0.38 to 0.51 captures the true proportion of supporters
2. 95% of the sample supports the plan
3. There is a 95% probability that p is between 0.38 and 0.51
A 95% confidence interval for the proportion of voters who support a plan is (0.48, 0.51). Which interpretation is right?
1. We are 95% confident that the interval from 0.48 to 0.51 captures the true proportion of supporters
2. 95% of the sample supports the plan
3. There is a 95% probability that p is between 0.48 and 0.51
A 95% confidence interval for the proportion of voters who support a plan is (0.46, 0.51). Which interpretation is right?
1. We are 95% confident that the interval from 0.46 to 0.51 captures the true proportion of supporters
2. 95% of the sample supports the plan
3. There is a 95% probability that p is between 0.46 and 0.51
A 95% confidence interval for the proportion of voters who support a plan is (0.50, 0.57). Which interpretation is right?
1. We are 95% confident that the interval from 0.50 to 0.57 captures the true proportion of supporters
2. 95% of the sample supports the plan
3. There is a 95% probability that p is between 0.50 and 0.57
A 95% confidence interval for the proportion of voters who support a plan is (0.47, 0.60). Which interpretation is right?
1. We are 95% confident that the interval from 0.47 to 0.60 captures the true proportion of supporters
2. 95% of the sample supports the plan
3. There is a 95% probability that p is between 0.47 and 0.60
A 95% confidence interval for the proportion of voters who support a plan is (0.33, 0.56). Which interpretation is right?
1. We are 95% confident that the interval from 0.33 to 0.56 captures the true proportion of supporters
2. 95% of the sample supports the plan
3. There is a 95% probability that p is between 0.33 and 0.56
A 95% confidence interval for the proportion of voters who support a plan is (0.33, 0.55). Which interpretation is right?
1. We are 95% confident that the interval from 0.33 to 0.55 captures the true proportion of supporters
2. 95% of the sample supports the plan
3. There is a 95% probability that p is between 0.33 and 0.55
A 95% confidence interval for the proportion of voters who support a plan is (0.43, 0.59). Which interpretation is right?
1. We are 95% confident that the interval from 0.43 to 0.59 captures the true proportion of supporters
2. 95% of the sample supports the plan
3. There is a 95% probability that p is between 0.43 and 0.59
A 95% confidence interval for the proportion of voters who support a plan is (0.35, 0.57). Which interpretation is right?
1. We are 95% confident that the interval from 0.35 to 0.57 captures the true proportion of supporters
2. 95% of the sample supports the plan
3. There is a 95% probability that p is between 0.35 and 0.57
A 95% confidence interval for the proportion of voters who support a plan is (0.40, 0.59). Which interpretation is right?
1. We are 95% confident that the interval from 0.40 to 0.59 captures the true proportion of supporters
2. 95% of the sample supports the plan
3. There is a 95% probability that p is between 0.40 and 0.59
A 95% confidence interval for the proportion of voters who support a plan is (0.49, 0.55). Which interpretation is right?
1. We are 95% confident that the interval from 0.49 to 0.55 captures the true proportion of supporters
2. 95% of the sample supports the plan
3. There is a 95% probability that p is between 0.49 and 0.55
A 95% confidence interval for the proportion of voters who support a plan is (0.36, 0.52). Which interpretation is right?
1. We are 95% confident that the interval from 0.36 to 0.52 captures the true proportion of supporters
2. 95% of the sample supports the plan
3. There is a 95% probability that p is between 0.36 and 0.52
A 95% confidence interval for the proportion of voters who support a plan is (0.46, 0.55). Which interpretation is right?
1. We are 95% confident that the interval from 0.46 to 0.55 captures the true proportion of supporters
2. 95% of the sample supports the plan
3. There is a 95% probability that p is between 0.46 and 0.55
A 95% confidence interval for the proportion of voters who support a plan is (0.43, 0.51). Which interpretation is right?
1. We are 95% confident that the interval from 0.43 to 0.51 captures the true proportion of supporters
2. 95% of the sample supports the plan
3. There is a 95% probability that p is between 0.43 and 0.51
A 95% confidence interval for the proportion of voters who support a plan is (0.42, 0.51). Which interpretation is right?
1. We are 95% confident that the interval from 0.42 to 0.51 captures the true proportion of supporters
2. 95% of the sample supports the plan
3. There is a 95% probability that p is between 0.42 and 0.51
A 95% confidence interval for the proportion of voters who support a plan is (0.49, 0.60). Which interpretation is right?
1. We are 95% confident that the interval from 0.49 to 0.60 captures the true proportion of supporters
2. 95% of the sample supports the plan
3. There is a 95% probability that p is between 0.49 and 0.60
A 95% confidence interval for the proportion of voters who support a plan is (0.32, 0.58). Which interpretation is right?
1. We are 95% confident that the interval from 0.32 to 0.58 captures the true proportion of supporters
2. 95% of the sample supports the plan
3. There is a 95% probability that p is between 0.32 and 0.58
A 95% confidence interval for the proportion of voters who support a plan is (0.30, 0.53). Which interpretation is right?
1. We are 95% confident that the interval from 0.30 to 0.53 captures the true proportion of supporters
2. 95% of the sample supports the plan
3. There is a 95% probability that p is between 0.30 and 0.53
A 95% confidence interval for the proportion of voters who support a plan is (0.46, 0.59). Which interpretation is right?
1. We are 95% confident that the interval from 0.46 to 0.59 captures the true proportion of supporters
2. 95% of the sample supports the plan
3. There is a 95% probability that p is between 0.46 and 0.59
A 95% confidence interval for the proportion of voters who support a plan is (0.44, 0.58). Which interpretation is right?
1. We are 95% confident that the interval from 0.44 to 0.58 captures the true proportion of supporters
2. 95% of the sample supports the plan
3. There is a 95% probability that p is between 0.44 and 0.58
A 95% confidence interval for the proportion of voters who support a plan is (0.47, 0.51). Which interpretation is right?
1. We are 95% confident that the interval from 0.47 to 0.51 captures the true proportion of supporters
2. 95% of the sample supports the plan
3. There is a 95% probability that p is between 0.47 and 0.51
A 95% confidence interval for the proportion of voters who support a plan is (0.44, 0.56). Which interpretation is right?
1. We are 95% confident that the interval from 0.44 to 0.56 captures the true proportion of supporters
2. 95% of the sample supports the plan
3. There is a 95% probability that p is between 0.44 and 0.56
A 95% confidence interval for the proportion of voters who support a plan is (0.39, 0.54). Which interpretation is right?
1. We are 95% confident that the interval from 0.39 to 0.54 captures the true proportion of supporters
2. 95% of the sample supports the plan
3. There is a 95% probability that p is between 0.39 and 0.54
A 95% confidence interval for the proportion of voters who support a plan is (0.34, 0.53). Which interpretation is right?
1. We are 95% confident that the interval from 0.34 to 0.53 captures the true proportion of supporters
2. 95% of the sample supports the plan
3. There is a 95% probability that p is between 0.34 and 0.53
A 95% confidence interval for the proportion of voters who support a plan is (0.49, 0.56). Which interpretation is right?
1. We are 95% confident that the interval from 0.49 to 0.56 captures the true proportion of supporters
2. 95% of the sample supports the plan
3. There is a 95% probability that p is between 0.49 and 0.56
A 95% confidence interval for the proportion of voters who support a plan is (0.30, 0.52). Which interpretation is right?
1. We are 95% confident that the interval from 0.30 to 0.52 captures the true proportion of supporters
2. 95% of the sample supports the plan
3. There is a 95% probability that p is between 0.30 and 0.52
A 95% confidence interval for the proportion of voters who support a plan is (0.43, 0.55). Which interpretation is right?
1. We are 95% confident that the interval from 0.43 to 0.55 captures the true proportion of supporters
2. 95% of the sample supports the plan
3. There is a 95% probability that p is between 0.43 and 0.55
A 95% confidence interval for the proportion of voters who support a plan is (0.32, 0.55). Which interpretation is right?
1. We are 95% confident that the interval from 0.32 to 0.55 captures the true proportion of supporters
2. 95% of the sample supports the plan
3. There is a 95% probability that p is between 0.32 and 0.55
A 95% confidence interval for the proportion of voters who support a plan is (0.50, 0.60). Which interpretation is right?
1. We are 95% confident that the interval from 0.50 to 0.60 captures the true proportion of supporters
2. 95% of the sample supports the plan
3. There is a 95% probability that p is between 0.50 and 0.60
A 95% confidence interval for the proportion of voters who support a plan is (0.31, 0.55). Which interpretation is right?
1. We are 95% confident that the interval from 0.31 to 0.55 captures the true proportion of supporters
2. 95% of the sample supports the plan
3. There is a 95% probability that p is between 0.31 and 0.55
A 95% confidence interval for the proportion of voters who support a plan is (0.39, 0.55). Which interpretation is right?
1. We are 95% confident that the interval from 0.39 to 0.55 captures the true proportion of supporters
2. 95% of the sample supports the plan
3. There is a 95% probability that p is between 0.39 and 0.55
A 95% confidence interval for the proportion of voters who support a plan is (0.44, 0.52). Which interpretation is right?
1. We are 95% confident that the interval from 0.44 to 0.52 captures the true proportion of supporters
2. 95% of the sample supports the plan
3. There is a 95% probability that p is between 0.44 and 0.52
A 95% confidence interval for the proportion of voters who support a plan is (0.48, 0.59). Which interpretation is right?
1. We are 95% confident that the interval from 0.48 to 0.59 captures the true proportion of supporters
2. 95% of the sample supports the plan
3. There is a 95% probability that p is between 0.48 and 0.59
A 95% confidence interval for the proportion of voters who support a plan is (0.49, 0.51). Which interpretation is right?
1. We are 95% confident that the interval from 0.49 to 0.51 captures the true proportion of supporters
2. 95% of the sample supports the plan
3. There is a 95% probability that p is between 0.49 and 0.51

Answers

A statistic: it describes the sample
All US college students
A parameter: it describes the whole population
Voluntary response bias: people with strong opinions choose to respond
Undercoverage: people without landlines cannot be chosen
It stays: a larger sample reduces random variability, not bias
$\frac{43}{184}$
A paired t test on the 24 differences (after − before)
$\frac{\sqrt{42}}{100}$
$- \frac{23}{3}$
$\frac{163}{2}$
$47$
An experiment: the researchers assign the treatment
A confounding variable: age affects both shoe size and reading
No: other differences between the groups could explain it
To balance other variables between the groups, so differences can be blamed on the treatment
A randomized block design, blocking by sex
$10$
The sample proportion p̂ (a statistic)
vary less around the population mean
The distribution of x̄ over all possible samples of size 25
$\frac{1}{20}$
$48$
A larger sample size
$\frac{49}{1000}$
$\frac{\sqrt{21}}{50} + \frac{3}{10}$
We are 95% confident that the interval from 0.48 to 0.52 captures the true proportion of supporters