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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

Tools don’t fail us; we fail to set them up for success. Any stats package will happily scramble your analysis if you paste unlabeled columns and hope for the best. Try this instead: keep tidy data, and dependent-variable columns and numeric factor columns with human-readable labels (e.g., “1=High-Impact, 2=No-Impact, 3=Personal Baseline”). Before any test, run

Every dataset whispers two stories. “Within” is the human noise floor: mood, sleep, coffee, quirks. “Between” is what your design invited to happen. ANOVA’s brilliance is ratio, not rhetoric: mean square between/mean square within. High ratio? Your manipulation likely mattered. Low ratio? Your signal is still stuck in the room hum. Build the craft by