You won’t always have the toolset to fit a model… but you can still read one. Start with the claim: “There is (or isn’t) an association/effect.” Translate it: if the null were true, how surprising is the observed pattern? That’s the p-value, not a truth meter but a rarity meter. Then anchor on effect size (η², ω², Cohen’s d, Cramer’s V): how big is the story, not just whether it exists. Finally, ask about design: how were groups defined, outcomes measured, assumptions checked? With those three lenses (surprise, size, and design) you can evaluate almost any result. You’re practicing statistical literacy, not software fluency. And literacy scales: the same questions make sense across t-tests, ANOVA, and contingency-table analyses. Don’t let tooling limit your understanding. Read with structure; decide with confidence.
