#110 — Comparing two sets of data is a fundamental process in statistical analysis, crucial for drawing meaningful conclusions across various fields. Whether it's for determining the success of an intervention, understanding market trends, or validating scientific research, the need for comparison arises.

This episode delves into the essence of data comparison, focusing on two prevalent statistical tests: the Student’s t-test and the Mann–Whitney U test. [1] Each test comes with its assumptions and applicability, making the choice between them critical depending on the nature of your data.

Read our related article to learn more about comparing multiple datasets. [2]

Resources:
1. Let’s Talk About Stats: Methods for Comparing Two Sets of Data. Available at: https://bitesizebio.com/19298/comparing-two-sets-of-data/
2. Let’s Talk About Stats: Getting the Most out of your Multiple Datasets with Post-hoc Testing. Available at: https://bitesizebio.com/19318/lets-talk-about-stats-getting-the-most-out-of-your-multiple-datasets-with-post-hoc-testing/