Discover the Latest in Biomedical Research Innovation

Most bench scientists reach for a t-test or ANOVA by reflex, but plenty of real experiments have a continuous dose, groups that differ at baseline, or nuisance variation eating the power. Multiple regression handles all three, and ANOVA and ANCOVA turn out to be special cases of it. This guide covers when regression earns its place, how to read coefficients honestly, the two covariate mistakes that invalidate an analysis, and why dose-response needs a 4PL curve, not a line.

Your treated and control wells started at different confluence, so comparing endpoints with a plain ANOVA measures your treatment effect plus the head start. ANCOVA fixes that by adjusting for a continuous covariate, and usually buys power too. This step-by-step ANCOVA protocol runs 24 numbered steps from covariate choice through reporting, with the assumption checks in the order they need to happen, including the parallel-slopes test most people skip.

Stop using linear regression for binary data! If your pre-clinical research involves "Yes/No" outcomes—like mouse survival, tumor presence, or assay viability—you need Binary Logistic Regression. This guide bridges the gap between complex math and bench science, explaining exactly how to run, validate, and interpret logistic models. Learn how to handle Odds Ratios (OR), avoid "Perfect Separation" errors in small cohorts, and satisfy the statistical standards of top-tier biome