Stats Final Review

assumption

A belief or statement taken for granted without proof.

between-groups estimate of the population variance

S2 between. an estimate of the variance of the population of individuals based on the variation among the means of the groups studied

variance of the distribution of differences between means

S2 difference

distribution of differences between means

distribution of differences between means of pairs of samples such that, for each pair of means, one is from one population and the other is from a second population

t test for dependent means

hypothesis-testing procedure in which there are two scores for each person and the population variance is not known

A t test for dependent means would be used to compare

the level of reading comprehension of students at the beginning and at the end of a speed-reading class.

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

26

t distribution

A distribution specified by degrees of freedom used to model test statistics for the sample mean, differences between sample means, etc.

two-tailed test

A hypothesis test in which rejection of the null hypothesis occurs for values of the test statistic in either tail of its sampling distribution.

one-tailed test

A hypothesis test in which rejection of the null hypothesis occurs for values of the test statistic in one tail of its sampling distribution.

One-directional hypothesis

A hypothesis that indicates that an independent variable will either have more of an effect on a dependent variable, or less. It can't be both.

repeated measures design

An experiment using a within-groups design in which participants respond to a dependent variable more than once, after exposure to each level of the independent variable.

weighted average

Average of data that adds factors to reflect the importance of different values.

main effect

In a factorial design, the overall effect of one independent variable on the dependent variable, averaging over the levels of the other independent variable.

The comparison distribution in a test for a single sample (N>1) is a distribution of ______

Means

The comparison distribution in a T test for dependent means (N>1) is a distribution of

Means of difference scores

nondirectional hypothesis

Predicts the existence of a relationship, not its direction

standard deviation of the distribution of differences between means

S difference

Which of the following statements is true about T distributions?

The larger the sample size, the more a t distribution resembles a normal curve.

within-groups degrees of freedom (denominator)

The number of degrees of freedom associated with the within-groups estimate of variance; equivalent to the number of cases minus the number of groups. df within.

degrees of freedom

The number of individual scores that can vary without changing the sample mean. Statistically written as 'N-1' where N represents the number of subjects.

In which of the following situations would a T test for independent means be conducted?

a comparison of scores of participants in a memory study where one group is assigned to learn the words in alphabetical order and another group is assigned to learn the words in order of length of the word

interaction effect

a result from a factorial design, in which the difference in the levels of one independent variable changes, depending on the level of the other independent variable; a difference in differences

t test

a statistical test used to evaluate the size and significance of the difference between two means

grouping variable

a variable that separates groups in analysis of variance

post-hoc tests

additional significance tests conducted to determine which means are significantly different for a main effect

factorial analysis of variance

analysis of variance for a factorial research design

two-way analysis of variance

analysis of variance for a two-way factorial research design

analysis of variance (ANOVA)

analysis of variance test used for designs with three or more sample means

between-groups degrees of freedom (numerator)

df between. The number of degrees of freedom associated with the estimate of between-groups variance; equivalent to the number of groups minus 1.

biased estimate

estimate of a population parameter that is likely systematically to overestimate or underestimate the true value of the population parameter

unbiased estimate of the population variance (S^2)

estimate of the population variance, based on sample scores, which has been corrected so that it is equally likely to overestimate or underestimate the true population variance

within-groups estimate of the population variance (S2 within)

estimate of the variance of the population of individuals based on the variation among the scores in each of the actual groups studied

two-way factorial research design

factorial research design in analysis of variance with two variables that each divide the groups

t test for a single sample

hypothesis-testing procedure in which a sample mean is being compared to a known population mean and the population variance is unknown

t test for independent means

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

pooled estimate of the population variance

in a t test for independent means, weighted average of the estimates of the population variance from two samples

When estimating the variance of a population from the sample, the sample variance cannot be used directly because

it tends to be slightly too small—it underestimates the population variance.

grand mean (GM)

mean of the group means

F distribution

positively skewed distribution derived from a sampling distribution of F ratios

difference scores

scores representing the difference between subjects' performance in one condition and their performance in a second condition

planned contrasts

statistical tests that allow us to test comparisons between groups that we predicted ahead of time. These tests have the added benefit of allowing you to combine two conditions so that you can compare it to a third

robust

strong

the main difference between a Z score and a T score is that

t scores are used when the population variance is unknown

F table

table of cutoff scores on the F distribution

t table

table of cutoff scores on the t distribution for various degrees of freedom, significance levels, and one- and two-tailed tests

marginal mean

the average of all participants on one level of the independent variable, ignoring the other independent variable

cell mean

the average score of the participants in a single cell

A t test for a single sample would be used to compare

the hours that "C" average students spend on Facebook each week compared with students in general.

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.

F ratio

the ratio of between-groups variance to within-groups variance

factorial research design

way of organizing a study in which the effects of two or more variables are studied at once by making groupings of every combination of the variables