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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.

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.

SD, SEM, and 95% CI are three different error bars answering three different questions, and they're not interchangeable. The standard error vs standard deviation confusion is where most figures go wrong: SEM is always the smallest, so it makes data look tighter than it is, which is why reviewers flag it most. This piece explains what each bar actually claims, shows one dataset drawn three ways, and tells you which to use, and when to just report SD with your n.

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