Analysis-plan to output consistency checklist
A compact review path for finding mismatches between the planned question and the result being reported.
Read articleResearch Methods Library
Quantitative decision guides
Practical guidance on study planning, power, measurement, outcome models, complex data, reproducibility, software workflows, and independent analysis validation.
What to check before reporting AI-generated statistics
Fluent code and explanations require the same independent checks as any other analysis, plus careful verification of invented assumptions.
Read articleHow to audit a statistical analysis before reporting it
Trace the chain from question to data, model, diagnostics, interpretation, and reproducible output.
Read articleReproducible quantitative research with R
A useful R workflow makes inputs, transformations, models, diagnostics, outputs, and environment assumptions inspectable.
Read articleSPSS output is not an analysis plan
Menus can execute procedures, but they cannot align the question, variables, assumptions, diagnostics, and interpretation.
Read articleFive signals your data are clustered
Shared sites, teams, people, households, and time points can make ordinary independence assumptions misleading.
Read articleWhat to decide about missing data before regression
Missingness affects the analysis population, estimand, uncertainty, and credibility long before a model is fitted.
Read articleChoosing a regression family from the outcome and data structure
Model choice begins with the estimand, outcome support, dependence, sampling, and missingness, not a significance threshold.
Read articleEFA vs CFA: choose from the research decision, not the software menu
Exploration and confirmation answer different questions and require different commitments before estimation.
Read articleReliability is not validity: a measurement decision guide
A consistent score can still fail to measure the intended construct or support the comparison a study needs.
Read articleA guide cannot see your full study
If the recommendation changes with your design, data structure, measurement, or existing output, discuss the statistical decision in context.
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