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Pre-register the primary question and outcomes. Define the minimal meaningful effect. Plan the model that answers the one big question (ANOVA for multi-group means; chi-square for categorical links; regression when predictors stack). Set your alpha and power targets. Specify how you’ll handle multiple comparisons (or avoid them with planned contrasts). Commit to reporting effect sizes […]

Statistics is an act of humility: say what the sample shows, then bound what you can infer about the population. Confidence intervals are your friend… they frame plausibility, not certainty. Replication is your ally… one sample is a clue; multiple, independent samples sketch the map. Effect sizes carry weight… small effects can matter, but only

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

t-tests are great at one thing: two-group comparisons. The moment you care about three or more conditions, t-tests turn into a whack-a-mole of pairwise guesses, each swing adding error. ANOVA reframes the puzzle. Instead of asking six small questions, it asks one: do these group means differ more than we’d expect from within-group noise? That

If one test risks a 5% false positive, what happens when you run ten? You don’t get ten times the truth… you get nearly a coin-flip chance of fooling yourself. Multiple testing quietly inflates Type I error… turning noise into “discoveries.” The fix isn’t magical; it’s methodological. Either adjust your alpha (Bonferroni, Holm, FDR) or

Prediction Is the Point  Good analysis upgrades your bets. If knowing X helps you predict Y better than chance, you’ve learned something you can use. Frame outputs as improved prediction and cost-aware decisions… not as proofs. Base Rates First  Before patterns, honor context. What’s common? What’s rare? Base rates anchor expectations, prevent over-reaction to noisy spikes, and

Most analysis failures aren’t math errors… they’re hygiene lapses. Build a boring, repeatable checklist: (1) Reconcile counts: do totals match inputs after filters and joins? (2) Confirm definitions: do variables mean what the decision requires, and are time windows aligned? (3) Pre-write the one-sentence takeaway before you compute: then see if the numbers actually support

“Statistically significant” means your data would be unlikely if nothing were happening. Leaders ask a different question: how big is the thing that is happening, and what does it buy us? Always pair claims of rarity with claims of magnitude. Translate the effect into absolute differences, expected counts, percentage points, or risk ratios that tie

Complex questions tempt infinite slicing. Start by defining the core contrast that actually answers the decision question. If extra categories blur the picture, collapse them thoughtfully (with a note explaining how). You’ll reveal the signal that stakeholders can act on. Then, and only then, add nuance back selectively: re-expand categories where stakes are high, where

Evidence is contrast. Every “yes” needs its “no,” every success its failure, every win its baseline of total attempts. Rates require denominators, and patterns require counter-patterns. Drop the “other half” and the tallest bars will always seduce you into over-reading. Keeping both sides also surfaces asymmetries that matter for action: maybe the success rate barely