Paired t test
Paired T-tests compare the difference in means between two sets of observations which are uniquely coupled e.g. exam scores for an individual before and after a training program. Paired T-tests are used only when you have two categorical ‘related’ groups as your independent variable and a continuous variable for dependent variable. The null hypothesis states that the difference in means is equal to 0, implying that any difference seen is due to chance.
This test produces 3 values, ‘t’ , ‘df’ and ‘p’ for 3 alternative hypotheses - that the true difference is greater than, lesser than, or not equal to 0 . ‘Df’ stands for degrees of freedom, which simply means the number of measurements in the data. ‘T’ is the value calculated in units of standard error which, when compared to a distribution of ‘t’ values, gives a ‘p’ value, which is the probability of obtaining that ‘t’ value or greater (or lesser, depending on the ‘tail’) if it was down to random chance.
Hence, the smaller the p-value, the more evidence there is that the observed difference is less due to chance.
Reduces confounding, increase statistical power
- Assume continuous dependent variable
- Assume normally distributed dependent variable
- Assume observations are independent
An example excel file can be downloaded below ‘ Paired T test example’
- There are four columns, ‘before’, ‘after’, ‘RandA’ and ‘Rand B’.
- There are 29 values in each column (these values are randomly generated, but could represent data before and after an intervention)
- A significant difference (i.e. a P-value less than 0.05) should be seen when comparing ‘before’ and ‘after’ columns, and no difference should be seen between ‘RandA’ and ‘RandB’
- Click on Analyze, upload your .csv or .xlsx file
- Specify the ‘before’ variable column
- Specify the ‘after’ variable column
- Under the results tab, ‘T’, ‘df’ and P values can be found under 3 different alternative hypotheses.
Written by Kevin Michell