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 »
Start with the basics: Did the sample sizes match what you expected? Do the averages feel plausible? Then go to the test: difference, interval, t, df, p. Say both formats for humans: bounds for leaders (“A is 0.5–4.5 higher”), estimate ± margin for analysts. End with an action verb: ship, staff, hold, or change. Reports
Read Output Like a Briefing, Not a Novel Read More »
Organizing your data in SPSS? Here’s the rule: one row = one person. Outcomes in one column. Group labels in another. Numbers are numbers (Scale). Names are names (Nominal). That’s it. Most “mystery errors” happen when headers get pasted as data and then numbers sneak in as text. Clean structure makes analysis boring… in the
The One Rule That Prevents Most Data Messes Read More »
If your real question is “Did Group A do better?” say that in advance. Direction locks the “tail” of your test. Declare “greater,” “less,” or “different” before the data tempts you. Software will gladly hand you any p-value you ask for, including the wrong one. Good science is a pre-commitment device: define the claim, then
Pick a Direction Before You Measure Read More »
Want to know if A beats B? First ask if doing A changes how you do B (or vice versa)? If the answer is “Yes”, use different people for each. Give one group A, the other B, and measure the same outcome once. That’s an independent two-sample comparison. No memory effects. No practice carryover. Just
Two Groups, One Honest Comparison Read More »
