PSYEXAM4

Which of the following is an example of a situation in which you could conduct a t test for independent means?

A comparison of scores of participants in a memory study in which one group learns the words in alphabetical order and another group learns the words in order of word length.

The comparison distribution for a t test for independent means is a:

distribution of differences between means

When conducting a t test for independent means, a typical research hypothesis might be:

the mean of Population 1 is greater than the mean of Population 2

In an analysis of variance, if the null hypothesis is rejected then:

the variation between sample means reflects the variation within the populations and the variation between the population means

In an analysis of variance, if the null hypothesis is true, then:

there is less variance among the sample means than if the null hypothesis was not true

All of the following statements are true about t tests EXCEPT:

they are used when the population variance is known

If a sample has 27 people in it, the degrees of freedom used in the formula to estimate the population variance would be:

26

A procedure used to compare more than two groups of scores, each of which is from an entirely separate group of people, is called a(n):

analysis of variance

In an analysis of variance, if the within-group variance estimate is about the same as the between-group variance estimate, then:

any difference between sample means is probably due to random sampling

The number of scores free to vary when estimating a population parameter is/are:

d. both A and B

When estimating the population's variance from the scores in a sample, you should

divide the sum of squared deviations by N-1 instead of N

An analysis of variance (ANOVA) differs from a test for independent means (t test) in that an ANOVA:

is used to compare the means of two or more groups whereas a t test is used to compare two groups

When estimating the variance of a population from the sample, you cannot use the sample's variance directly because:

it tends to be slightly too small

In a t test for independent means, the weighted average of the estimates of the population variance from two samples is known as the:

pooled estimate of population variance

Analysis of variance should only be done when:

population variances can be assumed to be equal

All of the following are true for both the t test for independent means AND the t test for dependent means, EXCEPT:

pretest-posttest experimental designs are common

In a t test for independent means, the square root of the variance of distribution of differences between means is known as the:

standard deviation of the distribution of differences between means

What is the main difference between Z score and t score?

t scores are used when the population variance is unknown

A local newspaper reports that young women in your town (i.e., a known population) sleep an average of only 5 hours per night. However, you think that women in your town who are enrolled at your college sleep more than that. You randomly select 200 women from your college and ask them to accurately record their hours of sleep. You find that they sleep an average of 7 hours per night. What test would you use to determine whether this mean difference is statistically meaningful?

t test for a single sample

The __________ is a hypothesis-testing procedure in which there are two separate groups of people tested and the population variance is not known.

t test for independent means

A "distribution of differences between means" can be thought of as a distribution of as:

the differences you get when you repeatedly draw a sample mean from one population and a sample mean from another population and subtract one mean from the other

You conduct a t test for independent means and reject the null hypothesis. This means that:

the mean of one sample is so far from the mean of the other sample that you decide the samples must come from populations with different means

S2Within" equals:

the population variance estimate based on the variation within each of the groups

When you do an analysis of variance:

you compare two estimates of the population variance

Because of the assumption that the population variances are equal, when you do an analysis of variance:

you figure an averaged estimate of the population variance