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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

Run an ANCOVA and a multiple regression on the same data, specified equivalently, and you get the same F, the same p, and the same coefficients. They are one model with two traditions, and dummy coding is the bridge. So the real question isn't which is correct but which framing makes your result clearest. This piece covers why they're equivalent, what genuinely differs, and how to describe your model so reviewers from either tradition can follow it.

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.

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 small p-value tells you a difference is probably real, not that it's big enough to matter. Statistical significance asks whether an effect is distinguishable from noise; biological significance asks whether it's large enough to change a cell, a patient, or a conclusion. Because the p-value shrinks as your sample grows, a big enough study can stamp p < 0.05 on a difference that means nothing. Here's why, with real examples, and what to report instead: effect size and confide