When the p-value (shown under "Pr>F") is greater than 0.05, then the variances are equal then read the "Pooled" section of the result.The test on Equality of Variances is given at the end, and is repeated below, Note that the results show both "Pooled" and "Satterthwaite" sections, which is based on sample variances check. Proc ttest data=read sides=2 alpha=0.05 h0=0 Note that the test is two-sided (sides=2), the significance level is 0.05, and the test is to compare the difference between two means (mu1 - mu2) against 0 (h0=0). In this demo example, two samples (control and treatment) are independent, and pass the Normality check. Two dependent samples and does not follow Normal distribution, suggest Signed Rank test.Two independent samples and does not follow Normal distribution, suggest WMW test.Two dependent samples and follow Normal distribution, suggest Paired T-test.When the assumptions are not met, other methods are possible based on the two samples: The two samples follow normal distributions, and can be done with Normality check.There is one continuous dependent variable and one categorical independent variable (with 2 levels).Infile "H:\sas\data\reading.csv" dlm=',' firstobs=2 Grade (continuous) ~ method (categorical: 2 levels) The problem is to test whether the two methods make a difference? The model you can set up for this problem is After they are trained with the method, their performance is measured as grades. Users will be randomly assigned either one method. There is a new method (treament, or t) and a standard method (control, or c). Suppose there is a study to compare two study methods and see how they improve the grades differently. One variable to be measured and compared between two conditions (samples). A common experiment design is to have a test and control conditions and then randomly assign a subject into either one. The idea of two sample t-test is to compare two population averages by comparing two independent samples. Compare two independent samples with t-test.
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