Cryptic labels waste cognition. Human labels return it. “Group A” is fog. “Early-season cohort” is a map. Same data, different friction. Communication is part of the method, not an afterthought. If you want people to reason well, lower the decoding cost. Name variables so a beginner can argue with them. Because argument is learning. And […]
Name Things Like You Want Them Understood Read More »
Ever run the right test on the wrong categories? It looks rigorous. It isn’t. Buckets are arguments in disguise. Lumpy bins make lumpy answers. Before you analyze, ask: what does each label assume? Do my groups mirror reality… or convenience? Align categories to the mechanism you care about. Then press “go.” Clean categories create clean
When Your Bins Betray Your Question Read More »
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 »
