The T-test is used to compare the means of two sets of normally distributed data. Thus, each set of data has a mean and standard deviation following a normal (or nearly normal) distribution that can be mathematically compared for statistical significance.
It is commonly applied to medical research in the following situations:
- Comparing a control arm vs. a treatment arm in a randomized trial (ie. 2 different/independent populations)
- Comparing a population at different times such as pre-intervention vs post-intervention (i.e. the same population before vs. after an intervention)
There is a formula (that you, hopefully, don’t need to memorize) used to compare each population’s mean, standard deviation, and quantity to calculate the t-test result. The t-test result is then used to determine the statistical significance (the “p” value). If the “p” value is <0.05 (typically) then one can conclude that there is a statistically significant difference between the populations.
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