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Choose One Eligible Outcome Result Without Cherry-Picking

Prespecify rules for instruments, time points, and analyses when a study reports several eligible results.

Compare Methods on the Same Simulated Datasets

Preserve replicate pairing when evaluating differences in performance and computational behavior.

Label Percentage Denominators in Tables and Charts

Make row, column, eligible-population, and multiple-response percentages distinguishable.

Check Exchangeability Before a Permutation Test

Match the allowed rearrangements to the design and null hypothesis rather than shuffling every row.

Report pilot estimates with useful uncertainty

Explain what a small pilot can estimate and why an imprecise effect should not become a definitive trial assumption.

Audit Treatment Overlap Before Trusting a Weighted Comparison

Identify where the observed data do not support the treatment contrasts required by the estimand.

Check Population Margins Before Poststratifying a Survey

Verify categories, reference populations and weighted totals before accepting calibrated weights.

Read Precision–Recall Results in Their Population Context

Connect positive predictive value and sensitivity to the event frequency and threshold being evaluated.

Flag Predictions Outside the Observed Predictor Support

Distinguish interpolation from unsupported combinations before presenting model-based predictions.

Plan What a Prediction Model Does When an Input Is Missing

Validate the actual missing-input workflow instead of assuming complete predictors at deployment.

Treat Recalibration as a Model Update That Needs Evaluation

Preserve the original validation result while testing a justified update to predicted probabilities.

Scale Predictors for Numerical Stability Without Losing Units

Record transformations and back-transform coefficients so numerical convenience does not change the reported effect.