statistics

This isn’t a trick you deploy once and forget. It’s a practice: ask a sharper question, collect what matters, model it honestly, decide out loud. Tools will change. Jargon will evolve. The work stays the same… reduce wishful thinking, increase useful action. When in doubt, return to first moves: name the null, set the stakes, […]

Set the Gate  Write the one question your model must answer. If you’re listing pairwise comparisons, you need ANOVA or planned contrasts… not whack-a-mole. Define “Meaningful” Before “Significant”    Pick the smallest effect that would change a choice. If your margin doesn’t steer behavior, you’re measuring trivia. Visual Proof of Life    Plot distributions and intervals before testing.

Write the primary question first. Define the smallest effect worth acting on. Choose the model that answers that question once (t-test, ANOVA, chi-square, regression). Fix alpha and power up front. If you must test multiple things, protect alpha (planned contrasts, Holm/FDR). Keep data tidy: one row per unit, clear variable names, human-readable labels. Preflight with

You don’t need the code to think statistically. Scan for four things: Surprise (the p-value under the null), Scope (degrees of freedom… how many comparisons the test effectively carried), Size (effect size with an interval), and Setup (design, sampling, measurement). If any link is weak, temper the headline. A tiny p with a tiny effect

T-tests shine when there are two groups. Add a third and you’re tilting at windmills… each pairwise test inflates your error, turning “maybe” into “must be.” ANOVA reframes the question: is between-group variation meaningfully larger than within-group noise? One gatekeeping test protects your alpha, then (if warranted) planned contrasts or honest post-hocs tell you where

A confidence interval isn’t “the range where the truth lives.” It’s a contract: repeat this method forever, and this style of interval would catch the truth at your chosen rate. That’s it. Want tighter bounds? Pay with more data or more discipline; don’t pay with wishful thinking. Before you compute anything, ask: what margin would

If you don’t name the null, your brain will. And it’s a generous storyteller. The null isn’t cynicism; it’s the control that keeps you from worshiping coincidence. “No difference. No association. No effect.” Boring? Good. That’s the point. Only when your data make that stance unlikely do you earn a new sentence. Begin by writing

The F Curve Humility  That sloping F curve is a humility lesson. It asks, “How often would chance alone throw a number like yours?” If the tail is thin where you landed, you likely touched the signal. If it’s thick, you likely touched your own impatience. Curiosity beats certainty; run the test, then earn the claim.

Fisher’s LSD and Tukey’s HSD aren’t enemies; they’re personalities. LSD is powerful and liberal… great at finding differences with three simple groups, riskier with many. Tukey balances caution and power… steady, trusted, and happiest with equal sample sizes. Bonferroni is the seatbelt you never unbuckle; Sidak, a sleeker cousin. Games-Howell is your friend when variances

Significance tests don’t create meaning… your design does. If your factor levels are cosmetic, ANOVA will dutifully compare cosmetics. The craft is identifying the construct that actually moves outcomes… then operationalizing it cleanly. Don’t toggle the nearest proxy; name the lever and build conditions that differ on that lever (not five things at once). Before