Stats Chapter 9

If other factors are held constant, what is the effect of increasing the sample size? It will decrease the estimated standard error and decrease the likelihood of rejecting the null hypothesis. It will decrease the estimated standard error and increase the likelihood of rejecting the null hypothesis. It will increase the estimated standard error and increase the likelihood of rejecting the null hypothesis. It will increase the estimated standard error and decrease the likelihood of rejecting the null hypothesis.

It will decrease the estimated standard error and increase the likelihood of rejecting the null hypothesis.

If other factors are held constant, what is the effect of increasing the sample variance? It will decrease the estimated standard error and increase the likelihood of rejecting the null hypothesis. It will increase the estimated standard error and decrease the likelihood of rejecting the null hypothesis. It will decrease the estimated standard error and decrease the likelihood of rejecting the null hypothesis. It will increase the estimated standard error and increase the likelihood of rejecting the null hypothesis.

It will increase the estimated standard error and decrease the likelihood of rejecting the null hypothesis.

To compute a one-sample t-test a researcher has to know several values. Which of the following is NOT a value that the researcher must know to compute this test? The sample variance The sample size The estimated standard error The population variance

The population variance

Which of the following is a fundamental difference between the t-statistic and a z-score? The t statistic uses the sample variance in place of the population variance. The t statistic is used for large samples only. The t statistic uses the sample mean in place of the population mean. The t statistic computes the standard error by dividing the standard deviation by n - 1 instead of dividing by n.

The t statistic uses the sample variance in place of the population variance.

A researcher conducts a hypothesis test using a sample from an unknown population. If the t statistic has df = 35, how many individuals were in the sample? n = 35 n = 36 n = 33 n = 34

n = 36

If you were to _____ the null hypothesis, you would say the result is statistically _____. reject; significant fail to reject; proven reject; nonsignificant fail to reject; significant

reject; significant

With α = .05, what is the critical t value for a one-tailed test with n = 15? t = 1.761 t = 2.145 t = 1.753 t = 2.131

t = 1.761

With α = .01, the two-tailed critical region for a t-test using a sample of n = 16 participants would have boundaries of _____. t = ±2.583 t = ±2.602 t = ±2.947 t = ±2.921

t = ±2.947

The decision to use a directional versus non-directional hypothesis test most directly affects _____. the critical value(s) the sample size the alpha level the degrees of freedom

the critical value(s)

Looking at the degrees of freedom value tells you something about _____. the directionality of the hypothesis test the alpha level the size of the sample the null hypothesis

the size of the sample

On average, what value is expected for the t-statistic when the null hypothesis is true? 0 -1.96 1 +1.96

0

Which set of characteristics will produce the smallest value for the estimated standard error? A large sample size and a large sample variance. A small sample size and a small sample variance. A large sample size and a small sample variance. A small sample size and a large sample variance.

A large sample size and a small sample variance.

If other factors are held constant, how does sample size influence the likelihood of rejecting the null hypothesis and measures of effect size such as r² and Cohen's d? A larger sample decreases both the likelihood and measures of effect size. A larger sample decreases the likelihood but has little influence on measures of effect size. A larger sample increases both the likelihood and measures of effect size. A larger sample increases the likelihood but has little influence on measures of effect size.

A larger sample increases the likelihood but has little influence on measures of effect size.

Under what circumstances can a very small treatment effect be statistically significant? If the sample size is small and the sample variance is large. If the sample size is big and the sample variance is small. If the sample size and the sample variance are both small. If the sample size and the sample variance are both large.

If the sample size is big and the sample variance is small.

When n is small (less than 30), how does the shape of the t distribution compare to the normal distribution? It is taller and narrower than the normal distribution. It is almost perfectly normal. It is flatter and more spread out than the normal distribution. There is no consistent relationship between the t distribution and the normal distribution.

It is flatter and more spread out than the normal distribution.