Statistics is an act of humility: say what the sample shows, then bound what you can infer about the population. Confidence intervals are your friend… they frame plausibility, not certainty. Replication is your ally… one sample is a clue; multiple, independent samples sketch the map. Effect sizes carry weight… small effects can matter, but only if your decision context says they do. And beware the mirage of significance with tiny variance or huge N; practically trivial differences can look mathematically loud. Flip the order: start from the decision you need to make. What difference would move you? What error rates are tolerable? Then choose a design and analysis that targets that decision. Inference is not a victory lap after data collection; it’s the plan you write before you step on the track.
