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79 decision guides
Choosing between GEE and mixed-effects models begins with the estimand: whether the scientific target is a population-average response or a subject-specific trajectory or conditional effect. This Resource explains how outcome type, within-person dependence, random effects, and missing-data assumptions shape that decision.
Read guideInteraction, effect modification, and subgroup significance are not interchangeable. Learn how effect scale, regression product terms, causal framing, and direct comparisons between subgroup effects determine the correct interpretation.
Read guideStatistical significance and clinical importance answer different questions. This Resource explains how to interpret effect magnitude, confidence intervals, clinically meaningful thresholds, sample size, and p values together before drawing a practical conclusion.
Read guideLearn how to analyze Likert scale data by distinguishing single ordered items from multi-item composite scores, then matching the measurement interpretation, estimand, study design, and statistical method to the research question.
Read guideSuperiority, noninferiority, and equivalence trials answer different clinical questions and require different hypotheses, boundaries, and interpretations. This Resource explains why a nonsignificant superiority result does not establish noninferiority or equivalence and shows how prespecified margins and confidence intervals determine what a study can support.
Read guideType I and Type II errors, alpha, beta, and statistical power are parts of one hypothesis-testing decision framework. This guide explains how they relate, why nonsignificant results do not prove the null hypothesis, and how power connects to effect size, variability, sample size, and study design.
Read guideInterim analyses become part of trial design whenever accumulating results can influence whether a clinical trial continues. This Resource explains why repeated efficacy looks require planned sequential methods, how efficacy and futility differ, what should be prespecified, and how early stopping affects interpretation, precision, and treatment-effect estimates.
Read guideA regression dataset can contain many rows without containing the same number of independent pieces of information. This Resource explains how to recognize clustered or correlated observations and how the research question, outcome distribution, clustering structure, and target interpretation guide the choice among GEE, linear mixed models, generalized linear mixed models, and other correlated-data methods.
Read guideLearn how to choose an appropriate effect-size metric for meta-analysis when studies report continuous outcomes, binary outcomes, or correlations. The Resource explains how the target estimand, measurement scale, substantive comparability, effect direction, and sampling variance affect the choice and interpretation of a common effect-size scale.
Read guideChoosing between fixed-effect and random-effects meta-analysis depends on what effects the studies represent and what population of effects the analysis is intended to describe. This Resource explains how that choice changes study weights, uncertainty, heterogeneity interpretation, subgroup analyses, meta-regression, and the scope of inference.
Read guideLearn how to choose among chi-square, binary logistic, multinomial, ordinal logistic, and matched categorical methods based on outcome structure, covariate adjustment, and dependence in the study design.
Read guideMeasurement invariance testing determines whether group comparisons are interpretable by assessing whether a construct is measured sufficiently equivalently across groups. This Resource explains the CFA hierarchy from configural through strict invariance and the distinct MICOM workflow required for PLS-SEM.
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.