Practical, careful guidance for planning, measurement, modeling, validation, and reproducible quantitative work.
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79 decision guides
Learn how to decide which variables to adjust for in observational studies using causal reasoning, conditional exchangeability, causal diagrams, and principled covariate selection. The Resource explains why statistical significance and outcome prediction alone cannot identify an appropriate causal adjustment set.
Read guideLearn how to interpret p-values, effect estimates, and confidence intervals together rather than relying on statistical significance alone. This Resource shows how evidence, magnitude, precision, and practical importance contribute to a defensible statistical conclusion.
Read guideLearn how to build and interpret reliable logistic regression models in medical research, from predictor handling and adjusted odds ratios to overfitting, internal validation, discrimination, and calibration.
Read guideA defensible data cleaning process establishes whether a dataset faithfully represents the observations, variables, measurements, and study design required by the research question. This checklist takes researchers from preserving source data and verifying coding through missingness, outliers, measurement, diagnostics, and final analysis readiness.
Read guideUnderstand how clinical trial data move from collection through SDTM tabulation datasets into ADaM analysis datasets, and how Define-XML documents those structures. The Resource also explains ADSL, traceability, and why standardized submission data are not automatically analysis-ready.
Read guideLearn when latent growth models are useful for repeated longitudinal outcomes, how intercept and slope factors represent individual trajectories, and how time coding, model fit, missingness, predictors, and alternative approaches affect interpretation.
Read guideChoosing Pearson vs Spearman should start with the relationship you want to measure. Pearson summarizes linear association; Spearman summarizes rank-order association. Learn how scatterplots, scale, outliers, curvature, and inferential assumptions change the decision.
Read guideYour design, measurement, data structure, assumptions, and existing output can change the appropriate statistical decision. If the choice is still unclear, discuss the study in context.