The primary assumption is that the differences between pairs are normally distributed. Because the pairs are closely matched, the variability between subjects is minimized.
Understanding Paired T Test Applications in Case Control Studies
For example, a psychologist might measure the anxiety levels of patients before and after a specific therapy session. The data must be continuous, such as temperature, time, weight, or blood pressure, and the differences between the pairs should be approximately normally distributed.
Effect size metrics should also be calculated to understand the magnitude of the change, as statistical significance does not always equate to practical importance. By analyzing the differences within each pair rather than the raw scores, the test reduces variability caused by individual characteristics, increasing statistical power.
Understanding Paired T Test Applications in Case Control Studies
If the two samples consist of different individuals—for example, measuring one group of people before a treatment and a different group of people after—the independent t-test is required. This design allows researchers to isolate the effect of the treatment itself, and the paired t-test is the appropriate tool to analyze the resulting difference in outcomes.
More About When would you use a paired t-test
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