Discover the Latest in Biomedical Research Innovation

Choosing between Welch's t-test, data transformation, and non-parametric tests like Mann-Whitney U is a critical decision that affects your p-values and publication chances. While standard t-tests often fail due to unequal variances, blindly switching to non-parametric tests can kill your statistical power. This guide simplifies the decision process, explaining why Welch's t-test should be your default and when to truly embrace ranking methods.

Are your p-values valid? The Shapiro-Wilk test is the "gold standard" for checking normality in biomedical research. This guide breaks down exactly how to perform the test in GraphPad Prism, SPSS, and R, how to interpret the results (p < 0.05 vs p > 0.05), and what to do when your data "fails" the test. Don't risk a rejected manuscript—master your statistical assumptions today.

Struggling with skewed, non-normal data in your biomedical research? Applying standard t-tests or ANOVA to data that doesn't fit the bell curve can lead to flawed conclusions. This guide cuts through the statistical jargon to provide a clear roadmap for researchers. Discover how to properly test for normality using Q-Q plots, when to apply data transformations, and how to use powerful non-parametric alternatives like the Mann-Whitney U or Kruskal-Wallis tests.