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.

Unlock the power of your biomedical data! Dose-response curves are vital, but non-linear relationships demand sophisticated analysis. This guide demystifies non-linear regression using GraphPad Prism. Learn to choose the right model, understand key parameters, set crucial constraints, and interpret results like confidence intervals and R². We cover data prep, global fitting for complex scenarios, and troubleshooting common issues like ambiguous fits. Master Prism for accurate

What's the best way to visualize time-to-event data in biomedical research? Enter the Kaplan-Meier survival curve. This powerful tool does more than just plot survival; it brilliantly handles the number one challenge in clinical studies: incomplete or "censored" data. This complete tutorial breaks down everything you need to know. We guide you step-by-step through the calculation, how to interpret the "step" plot, and how to use the log-rank test to see if your results are di

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.