Discover the Latest in Biomedical Research Innovation

An audit of 200 published animal studies found only 22% had replicated the right thing, and 46% counted repeated measurements of one biological unit as independent samples. That error, pseudoreplication, inflates your n, shrinks your error bars, and manufactures significance that won't replicate. Here's what separates technical vs biological replicates, why the mistake inflates false positives, and what actually counts as a biological replicate in cell culture.

Your p-value can confirm an effect is real and say nothing about whether it's big enough to matter. Effect size answers that second question, and for a two-group comparison Cohen's d is the standard measure: the mean difference divided by the pooled standard deviation. This guide covers how to calculate and report Cohen's d, why to report it alongside every p-value and confidence interval, and how to interpret it without leaning on the 0.2/0.5/0.8 benchmarks that often don't

Stop using correlation to validate your methods! The Bland-Altman plot is the gold standard for method comparison, revealing bias and error that simple correlation hides. This ultimate guide covers the math, step-by-step construction, interpretation of "Limits of Agreement," and advanced troubleshooting for proportional bias and non-normal data. Master the art of statistical agreement today.