Discover the Latest in Biomedical Research Innovation

Cut your finger and the wound closes because skin cells crawl inward until they meet. That is cell migration, and the scratch assay is how labs measure it. A plain-language guide to what the experiment does, why it appears in cancer, wound healing and device research, and what changed when its two manual steps became mechanical.

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.

Hunted through Prism's Analyze menu for ANCOVA? It isn't there, and GraphPad's own FAQ confirms they've never shipped one. Prism can still do it, through two documented routes most users never find: the simple-linear-regression slopes test for the parallel-slopes assumption, and multiple regression with dummy-coded groups for the adjusted comparison. Here are both step by step, plus an honest comparison with describing the same analysis in plain language instead.

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.

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.

A no-fluff, step-by-step scratch assay imaging protocol for images your software can actually measure. Twenty-six numbered steps take you from pixel calibration and Köhler alignment, through Time 0 capture with locked exposure, to returning to identical fields at later time points. Includes an equipment table, an acceptance-criteria checklist to run before you analyze, and a troubleshooting table mapping every common symptom to the step that fixes it.