What if success starts with a calendar… not capability? A line in the sand. Older on one side. Younger on the other. Tiny advantages masquerading as talent. Are we measuring skill… or timing? Data can tell you. Compare what you saw to what you’d expect if timing didn’t matter. When those don’t match, the story […]
The Hidden Leverage of Cutoffs Read More »
Significance is a siren. Residuals are a flashlight. Big picture: “something differs.” Cell by cell: “this is where.” Overrepresented. Underrepresented. That’s the texture of truth. Don’t stop at the headline. Trace the imbalance to its cause. What rule, habit, or policy would produce exactly this pattern? Change that… not everything. Precision beats drama. Want better
Residuals Are Where the Story Leaks Out Read More »
One-sentence test choice  Write the test you’ll use in one sentence that a smart non-statistician would accept. If you can’t, you’re not ready to analyze. Direction audit  State your directional hypothesis on paper. Circle the observation that would make you retract it. If you can’t find one, you don’t have a hypothesis… you have a preference. Ambiguity
Eight Tiny Experiments for Choosing Tests That Tell the Truth Read More »
Identical values can yield opposite conclusions when the design changes from one-sample to two-sample to paired. That’s not statistics being fickle… it’s statistics being literal. The tool answers exactly the question you ask. So build an audit trail: assignment method, who did which condition, counterbalancing, timestamps, preprocessing steps. Block mistakes with human labels, not just
If the same numbers told three different stories, which would you trust? Read More »
People don’t remember averages… they remember the peak and the end. That’s why a longer, slightly softened finish can feel “better” than a shorter, brutal stop. Design your endings on purpose: a cool-down set, a last slide that resolves the question, a final five minutes of easy wins. If repetition matters, engineer the memory that
What story will your ending make you believe? Read More »
Ambiguity taxes teams. In perception, confusion clusters around the boundary; in projects, it clusters around vague handoffs, soft deadlines, and unlabeled columns. The cure isn’t more data… it’s sharper signals. State the decision rule. Define the unit of analysis. Specify which rows belong to the same person. Then over-communicate once and measure the rework you
Where do people get confused… and what would clarity cost? Read More »
A one-tailed test is a promise: “Reality will move this way, not that way.” Promises should cost something. If your theory, preregistration, or prior evidence can’t justify a direction, you haven’t earned the efficiency of one tail. Use two and pay the price. But if your model truly points one way (and you’d publish a
Have you earned a direction? Read More »
Every analysis answers a question… but not always the one you think. One group against a benchmark answers “Do we clear this bar?” Two independent groups answer “Which approach works better, on average?” The same people measured twice answers “Do people change when they face both conditions?” Misalign the design and the software will still
What problem are you actually testing? Read More »
One Row, One PersonWhen one person lives across two rows, your analysis lies. One row per person, outcome in one column, group in another, rinse and repeat. That simple rule turns tangled sheets into answers you can trust. Name It So Future-You Smiles“var0001” is how mistakes breed. “score,” “group,” “time_ms” is how clarity spreads. Label
Eight Tiny Experiments for Actionable SPSS Read More »
“Statistically significant” is about surprise under a boring world. Leaders need size under the real world. Always pair a rarity claim with a magnitude claim, and translate it: small, moderate, large—plus what it means for a real decision. Numbers become persuasive when they travel as consequences.
Size the Effect, Not Just the Surprise Read More »