Discover the Latest in Biomedical Research Innovation

If every measurement comes from a different animal, use ordinary two-way ANOVA. If the same animals are measured at every time point with nothing missing, use repeated measures ANOVA. If even one value is missing, use a mixed-effects model. Here is how to match the model to your design, what each mistake costs, and how to set each one up in GraphPad Prism.

Your drug works in wild-type mice and not in the knockout, so the effect depends on the gene. That conclusion does not follow, and in one major review of top journals, half the papers that needed the right test got it wrong. This two-way ANOVA interaction SOP runs the correct analysis in GraphPad Prism, from data entry to a reportable result, with a worked example in which the naive reading and the right answer disagree.

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.

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

'P < 0.05'? This single number dictates whether a new drug is 'effective' or a finding is 'significant,' but what does it actually mean? P-values are perhaps the most misunderstood concept in biomedical data analysis. This article strips away the jargon. We'll explain exactly what a p-value is (hint: it's a 'measure of surprise'), how to interpret that 0.05 threshold, and the crucial, often-missed difference between 'statistical significance' and 'clinical significance.'