I. Inferences about Two Means

This chapter introduces hypothesis testing for two sets of data. In one type of test, the data is independent. In the second, the data is dependent.

The independent-measures hypothesis test allows researchers to evaluate the mean difference between two populations using data from two separate samples. The identifying characteristic of the independent-measures design is the existence of two separate or independent samples. This design can be used to test for mean differences between two distinct populations (such as mean versus women) or between two different treatment conditions (such as drug versus no-drug).

A dependent-samples t test (a.k.a. matched or paired-samples, matched-pairs, samples, or subjects, simple repeated-measures or within-groups, or correlated groups) assesses whether the

mean difference between paired/matched observations is significantly different from zero. That is, the dependent-samples t test procedure evaluates whether there is a significant difference

between the means of the two variables (test occasions or events). This design is also referred to as a correlated groups design because the participants in the groups are not independently

assigned. The participants are either the same individuals tested (assessed) on two occasions or under two conditions on one measure, or there are two groups of participants that are matched

(paired) on one or more characteristics (e.g., IQ, age, gender, etc.) and tested on one measure.