After reading and studying the material for week13, summarize the key terms and concepts discussed.

T Test for Independent Samples - Chapter 10 Independent-Measures Designs The independent-measures hypothesis test allows researchers to evaluate the mean difference between two populations using the data from two separate samples. The identifying characteristic of the independent-measures or between-subjects design is the existence of two separate or independent samples. Thus, an independent-measures design can be used to test for mean differences between two distinct populations (such as men versus women) or between two different treatment conditions (such as drug versus no-drug). The independent-measures design is used in situations where a researcher has no prior knowledge about either of the two populations (or treatments) being compared. In particular, the population means and standard deviations are all unknown. Because the population variances are not known, these values must be estimated from the sample data. Hypothesis Testing with the Independent-Measures t Statistic As with all hypothesis tests, the general purpose of the independent-measures t test is to determine whether the sample mean difference obtained in a research study indicates a real mean difference between the two populations (or treatments) or whether the obtained difference is simply the result of sampling error. Remember, if two samples are taken from the same population and are given exactly the same treatment, there still will be some difference between the sample means. This difference is called sampling error (see Figure 1.2 in your textbook). The hypothesis test provides a standardized, formal procedure for determining whether the mean difference obtained in a research study is significantly greater than can be explained by sampling error. To prepare the data for analysis, the first step is to compute the sample mean and SS (or s, or s2) for each of the two samples. The hypothesis test follows the same four-step procedure outlined in Chapters 8 and 9. 1. State the hypotheses and select an level. For the independent-measures test, H0 states that there is no difference between the two population means. 2. Locate the critical region. The critical values for the t statistic are obtained using degrees of freedom that are determined by adding together the df value for the first sample and the df value for the second sample. 3. Compute the test statistic. The t statistic for the independent-measures design has the same structure as the single sample t introduced in Chapter 9. However, in the independent-measures situation, all components of the t formula are doubled: there are two sample means, two population means, and two sources of error contributing to the standard error in the denominator. 4. Make a decision. If the t statistic ratio indicates that the obtained difference between sample means (numerator) is substantially greater than the difference expected by chance (denominator), we reject H0 and conclude that there is a real mean difference between the two populations or treatments.