Practical, careful guidance for planning, measurement, modeling, validation, and reproducible quantitative work.
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79 decision guides
A significant Shapiro–Wilk test does not automatically mean a researcher should switch to a nonparametric test. This synthetic case shows how the research question, measurement scale, design, skewness, outliers, robustness, and the hypothesis tested by Mann–Whitney should guide the decision.
Read guideA synthetic case study showing how to choose a defensible statistical test after data collection by moving from the research question and target inference through variable type, dependence, study design, assumptions, and interpretation. The case shows why three independent groups with one quantitative outcome lead to one-way independent-groups ANOVA while an observational design limits causal inference.
Read guideA synthetic power and sample-size case study showing how to justify a proposed sample size when the expected effect is uncertain. It demonstrates how meaningful effects, variability, power, attrition, feasibility, and sensitivity analysis combine into a defensible planning rationale.
Read guideUnderstand what nominal, ordinal, interval, and ratio measurement allow you to say about data, how measurement level affects descriptive statistics and candidate analytical methods, and why numerical coding does not change a variable’s underlying measurement properties.
Read guideA statistically significant regression can still be poorly specified. This synthetic case study works through linearity, residual behavior, constant variance, normality, multicollinearity, outliers, leverage, influence, overall fit, and coefficient interpretation—and shows how the substantive conclusion changes after diagnostics.
Read guideThis synthetic case study follows a binary logistic regression analysis from outcome coding to odds ratios, confidence intervals, diagnostics, predicted probabilities, and defensible interpretation. It shows why a binary outcome changes the modeling strategy and why odds ratios should not be interpreted as probability ratios or causal effects.
Read guideA large sample does not automatically represent a population. This Resource explains how convenience and probability sampling differ, why sample size and sampling design answer different questions, and what conclusions each design can support.
Read guideMissing values can affect who contributes to an analysis, the precision of estimates, and the population represented by the results. This Resource explains how MCAR, MAR, and MNAR assumptions relate to complete-case analysis, multiple imputation, likelihood-based methods, and sensitivity analysis.
Read guideA synthetic three-group intervention study shows why ANOVA and ANCOVA answer different questions when participants differ at baseline. Compare raw and adjusted group means, examine the baseline covariate and regression-slope assumption, and see why ANCOVA cannot repair a weak research design.
Read guideRepeated measurements from the same participant are dependent observations. This synthetic case study shows when repeated-measures ANOVA is appropriate, how to diagnose and address sphericity, how to interpret time and group effects, and why missing follow-up can change the preferred analysis.
Read guideA significant normality test is not, by itself, a reason to switch automatically to a nonparametric procedure. This guide explains how to choose among parametric, rank-based, and transformed analyses by considering the research question, study design, inferential target, and assumptions.
Read guideA significant ANOVA shows that population means are not all equal, but it does not identify which groups differ. This Resource provides a practical workflow for choosing planned contrasts, exploratory multiple comparisons, multiplicity control, and defensible reporting.
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.