Pre-register the primary question and outcomes. Define the minimal meaningful effect. Plan the model that answers the one big question (ANOVA for multi-group means; chi-square for categorical links; regression when predictors stack). Set your alpha and power targets. Specify how you’ll handle multiple comparisons (or avoid them with planned contrasts). Commit to reporting effect sizes and intervals alongside p-values. Inspect assumptions with purpose: normality of residuals, homogeneity of variance, independence by design. Visualize before and after modeling… plots reveal patterns that stats formalize. Document deviations from plan and why. End with a decision statement: “Given X, we will do Y.” These guardrails don’t slow you down; they keep you on the road when curiosity and confirmation bias try to tug the wheel.
