BLOG

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 […]

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

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

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

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

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,

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

“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

Don’t let noise pick your narrative. If theory says “up,” preregister a one-sided test (greater than 0) before collecting data. If you genuinely don’t know, choose two-sided (not equal to 0). One-sided buys power only when you commit; post-hoc direction isn’t power, it’s wishful thinking. In all cases, the null is still “effect = 0.”

Alpha = .05 is not sacred. It’s a policy choice. What’s the cost of a false alarm here… money, time, safety, trust? Choose alpha to match reality: .10 for quick screens, .05 for routine work, .01 (or .001) when stakes are steep. Declare it before the run. Then compare: p below alpha means you’ll proceed;