Discover the Latest in Biomedical Research Innovation

When medical residents were surveyed on interpreting a p-value, 88% were confident they understood it and 100% got it wrong. That gap is why p-value mistakes survive peer review: nobody thinks they're making one. This piece covers the five most common errors in biomedical papers, several measured at scale in real journals: the inverse probability fallacy, the replication fallacy, 'trending toward significance', p-hacking, and treating 0.05 as a bright line.

A no-fluff, step-by-step cell imaging protocol for microscopy images you can actually quantify. Eighteen numbered steps take you from Köhler setup and pixel calibration, through setting exposure on your brightest sample so nothing saturates, to acquiring the whole set under locked, identical settings. Works for brightfield, phase contrast, and fluorescence. Includes an equipment table, an acceptance-criteria checklist, and a troubleshooting table mapping each symptom to the s

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

Struggling to choose between t-SNE and UMAP for your single-cell data? UMAP has become the industry standard due to its speed and global structure preservation, but t-SNE still offers unique advantages for local granularity. This guide breaks down the mathematical differences, "Chari & Pachter" controversy, and proper parameter tuning to help you create scientifically accurate visualizations.