Conducting hypothesis tests for the difference between means with paired data

This tutorial covers the steps for computing hypothesis tests for the mean difference of paired data in StatCrunch. To begin, load the Weight Loss Program data set, which will be used throughout this tutorial. The data set is for an fictitious weight loss program. Each of the 15 rows represent a participant in the program. The Weight Before column is the individualâ€™s weight (in pounds) before undergoing the weight loss program, and the Weight After column is the same individualâ€™s weight (in pounds) after undergoing the weight loss program. This tutorial will cover using paired T methods for comparing the mean difference in weight before and after the program with this raw data set.

Performing a paired T hypothesis test

Is there a significant decrease in the typical weight of a program participant? This can be tested by conducting a paired T hypothesis test for the difference between mean weight after the program and the mean weight before the program. To compute the appropriate paired T hypothesis test results, choose the Stat > T Stats > Paired menu option. Select the Weight After column for Sample 1 in and select the Weight Before column for Sample 2 in. Under Perform, the Hypothesis test for μ_{D} = μ_{1} - μ_{2} is selected by default. Leave the null value at 0 to directly compare the mean difference. Change the alternative hypothesis to < to test if the mean weight decreased after the weight loss program. When the difference in means as specified is less than 0, this implies that the mean weight after the program is less than the mean weight before the program. Click Compute! to view the hypothesis test results. The output table below provides various statistics from this test including the test statistic and the P-value.

Always Learning
Pearson