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.

Struggling to choose between Tukey's HSD and Bonferroni for your biomedical research? Stop guessing. This guide breaks down exactly when to use each post-hoc test to avoid false positives and maximize statistical power. Learn why Tukey is best for "all-vs-all" exploration while Bonferroni shines in planned comparisons. Perfect for optimizing your Western blot and assay data analysis.

Running 10,000 gene expression tests? You might get 500 false positives by random chance. This is the "multiple hypothesis testing problem," and it's a critical flaw in modern biomedical research. The traditional fix, the Bonferroni correction, is so strict it often causing you to miss real discoveries. But a more powerful, modern method—the False Discovery Rate (FDR)—offers a revolutionary trade-off: what if you could control the proportion of false positives, not just the c