Discover the Latest in Biomedical Research Innovation

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.

In biomedical research, confusing standard deviation (SD) and confidence intervals (CI) is a common pitfall. Do you know the difference? One describes the spread of your sample data, while the other estimates the precision of a finding for the entire population. This guide breaks down the essential distinction, showing you how to use SD to describe your sample and CIs to infer your results, ensuring your data analysis is precise and powerful.