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Reviewers examining 100 clinical papers found 65% needed revisions just to explain what their statistical tests were for, and 64% had omitted effect sizes entirely. Statistical reporting problems are rarely about running the wrong analysis; they're about not writing down enough for a reader to judge it. Here's what belongs in the section, built on the SAMPL guidelines: seven things to state in your methods, the results formats that draw fewest queries, and a template you can

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.

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.