#Stata mean by group how to#

There are four 'assumptions' that underpin the paired t-test.

However, before we introduce you to this procedure, you need to understand the different assumptions that your data must meet in order for a paired t-test to give you a valid result.

#Stata mean by group how to#

In this guide, we show you how to carry out a paired t-test using Stata, as well as interpret and report the results from this test. Note: In Stata 12, you will see that the paired t-test is referred to as the 'Mean-comparison test, paired data', whereas in Stata 13, it comes under 't test (mean-comparison tests)'. Specifically, you use a paired t-test to determine whether the mean difference between two groups is statistically significantly different to zero. Alternately, you could use a paired t-test to understand whether there was a difference in smokers' daily cigarette consumption 6 week after wearing nicotine patches compared with wearing patches that did not contain nicotine, known as a 'placebo' (i.e., your dependent variable would be 'daily cigarette consumption', and your two related groups would be the two different 'conditions' participants were exposed to that is, cigarette consumption values after wearing 'nicotine patches' (the treatment group) compared to after wearing the 'placebo' (the control group)). For example, you could use a paired t-test to understand whether there was a difference in managers' salaries before and after undertaking a PhD (i.e., your dependent variable would be 'salary', and your two related groups would be the two different 'time points' that is, salaries 'before' and 'after' undertaking the PhD). The paired t-test, also referred to as the paired-samples t-test or dependent t-test, is used to determine whether the mean of a dependent variable (e.g., weight, anxiety level, salary, reaction time, etc.) is the same in two related groups (e.g., two groups of participants that are measured at two different 'time points' or who undergo two different 'conditions').