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 »
Computers are great at arithmetic. People are great at judgment. Give the calculator the heavy lifting, keep the meaning for yourself. Whether you use SPSS, Excel, or a notebook, the workflow is the same: define the question, choose the right test, run it, then decide. The software can’t pick your hypothesis, your confidence level, or
Let the Tool Lift, You Steer Read More »
Big p = “Not Yet” A large p-value doesn’t crown the null; it says today’s evidence can’t clear your risk line (alpha). Treat it as “not yet.” Tighten measures, strengthen design, or raise n. Then rerun. Small p, Small Claim A tiny p-value rejects “no effect.” It doesn’t prove a big effect, a useful effect,
Eight Tiny Experiments for P-Values with Purpose Read More »
One result is a flare, not a sunrise. Surprising claims (delightful or uncomfortable) earn replication. Make round two stricter: preregister, tighten measures, reduce researcher degrees of freedom, vary the setting or sample. If it’s real, it travels. If it’s fragile, you just saved months of theater. Build replication into the roadmap like QA: default, not
Replicate on Purpose Read More »
“Statistically significant” and “worth doing” are different currencies. A huge sample can make dust look like diamonds. After p beats alpha, ask: How big is the effect and does it justify action? Will a 0.4% lift cover engineering time, UX complexity, support load, and cognitive tax? Flip it, too: a chunky, user-loving effect with borderline
Small p, Tiny Effect? Ask the Second Question Read More »
