Statistics 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 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 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 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.

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 = 36

If you were to _____ the null hypothesis, you would say the result is statistically _____.

reject; significant

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

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 = ±2.947

The decision to use a directional versus non-directional hypothesis test most directly affects _____.

the critical value(s)

Looking at the degrees of freedom value tells you something about _____.

the size of the sample

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

0

Which set of characteristics will produce the smallest value for the estimated standard error?

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 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 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 flatter and more spread out than the normal distribution.