If wrong ideas are normal, why do they still derail people? Not because they appear… but because they stay unchallenged. Your intuition is not a liar; it’s a fast draft. Cognitive work begins when the draft becomes visible. Silence hides the model you’re using. And the model determines what you notice, what you ignore, and […]
Wrong Ideas Aren’t Dangerous… Unspoken Ones Are Read More »
What’s the fastest way to fail at something you could absolutely do well? Start by doing the work… before you decide what the work is. Most “hard problems” are just poorly framed problems wearing a costume. Your mind grabs the first handle it sees (distance, steps, details)… and then it commits. That commitment is expensive.
Change the Frame Before You Grind Read More »
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,
Closing Note: Statistics as a Practice (Not a Trick) Read More »
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.
Eight Tiny Experiments for Decision-Ready Stats – the Sequel Read More »
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
Guardrails for Honest Testing (A Compact Playbook) Read More »
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
Read Results Like a Pro (Even Without the Software) Read More »
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
One Test, Many Means: Why ANOVA Exists Read More »
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
Confidence Intervals: Promises About Process, Not Fortune-Telling Read More »
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
Start With the Null… or You’ll Start With a Story Read More »
