#Minitab t test how to

Since the same participants were measured at these two time points, the groups are related. For example, you might have measured 100 participants' salary in US dollars (i.e., the dependent variable) before and after they took an MBA to improve their salary (i.e., the two 'time points' where participants' salary was measured – 'before' and 'after' the MBA course – reflect the two 'related groups' of the independent variable). The reason that it is possible to have the same participants in each group is because each subject has been measured on two occasions on the same dependent variable. 'Related groups' indicates that the same participants are present in both groups.

Assumption #2: Your independent variable should consist of two categorical, 'related groups' or 'matched pairs'.If you are unsure whether your dependent variable is continuous (i.e., measured at the interval or ratio level), see our Types of Variable guide.

Examples of such continuous variables include height (measured in feet and inches), temperature (measured in oC), salary (measured in US dollars), revision time (measured in hours), intelligence (measured using IQ score), firm size (measured in terms of the number of employees), age (measured in years), reaction time (measured in milliseconds), grip strength (measured in kg), power output (measured in watts), test performance (measured from 0 to 100), sales (measured in number of transactions per month), academic achievement (measured in terms of GMAT score), and so forth.

Assumption #1: Your dependent variable should be measured at the continuous level (i.e., they are interval or ratio variables).

Assumptions #1 and #2 are explained below: If these assumptions are not met, there is likely to be a different statistical test that you can use instead. However, you should check whether your study meets these two assumptions before moving on. You cannot test the first two of these assumptions with Minitab because they relate to your study design and choice of variables. The paired t-test has four 'assumptions'. 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.

#Minitab t test how to

In this guide, we show you how to carry out a paired t-test using Minitab, as well as interpret and report the results from this test. Alternately, you could use a paired t-test to understand whether there is a difference in athletes' 100m sprint times when using a protein supplement compared to not using a supplement (i.e., the dependent variable would be '100m sprint time', and the two related groups would be the two different 'conditions' participants were exposed to that is, 100m sprint times when taking the protein supplement (condition A) compared 100m sprint times when not taking a supplement (condition B)). The paired t-test (also known as the paired-samples t-test or dependent t-test) determines whether there is a statistically significant difference in the mean of a dependent variable between two related groups.įor example, you could use a paired t-test to determine whether there is a difference in students' test anxiety before and after undergoing a hypnotherapy programme designed to reduce stress (i.e., the dependent variable would be 'test anxiety', and the two related groups would be the two different 'time points' that is, test anxiety 'before' and 'after' undergoing the hypnotherapy programme).