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Browse all available quizzes, quizbooks, articles, textbooks, SynLearn templates, and SynPrep practice tracks.

1890 learning items

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Showing 517–528 of 1890 items

Quiz

Adjustment, Matching & Propensity Weighting

Develop practical judgment in adjustment, matching & propensity weighting through 20 original single-select and multiselect problems. Apply standardization, matching and propensity weighting; compute target-specific weights, assess support and balance, and understand doubly robust estimation with its limits. Begin with concepts, then work through calculations, diagnostic scenarios and assumption-sensitive challenges. Prerequisites: probability, regression, statistical inference and the preceding syllabus topics. Detailed worked solutions explain the reasoning and each distractor. Intended easy-to-hard order is retained in the course record; app attempts may reorder questions. Cover: Photo by Kampus Production on Pexels Further reading: [adjust](https://mixtape.scunning.com/05-causal_cross_sectional_designs). Additional methods: [Oxford AIPW notes](https://www.stats.ox.ac.uk/~evans/APTS/dr.html) and [Overlap weights](https://academic.oup.com/aje/article/188/1/250/5090958).

statistics

Oct 26, 2026

20 questions

Quiz

Potential Outcomes, DAGs & Target Trials

Develop practical judgment in potential outcomes, dags & target trials through 20 original single-select and multiselect problems. Build causal estimands and DAG reasoning, distinguish confounders from mediators and colliders, and align target-trial eligibility, treatment and follow-up. Begin with concepts, then work through calculations, diagnostic scenarios and assumption-sensitive challenges. Prerequisites: probability, regression, statistical inference and the preceding syllabus topics. Detailed worked solutions explain the reasoning and each distractor. Intended easy-to-hard order is retained in the course record; app attempts may reorder questions. Cover: Photo by Mikhail Nilov on Pexels Further reading: [potential](https://mixtape.scunning.com/04-potential_outcomes_and_randomization), [adjust](https://mixtape.scunning.com/05-causal_cross_sectional_designs), [trial](https://pmc.ncbi.nlm.nih.gov/articles/4832051/), [dag](https://mixtape.scunning.com/03-directed_acyclical_graphs).

statistics

Oct 26, 2026

20 questions

Quiz

Measurement Error & Misclassification

Develop practical judgment in measurement error & misclassification through 20 original single-select and multiselect problems. Explore attenuation under explicit assumptions, repeat measurements, validation transport, binary classification errors and sensitivity to imperfect covariates. Begin with concepts, then work through calculations, diagnostic scenarios and assumption-sensitive challenges. Prerequisites: probability, regression, statistical inference and the preceding syllabus topics. Detailed worked solutions explain the reasoning and each distractor. Intended easy-to-hard order is retained in the course record; app attempts may reorder questions. Cover: Photo by Mikhail Nilov on Pexels Further reading: [error](https://www.stratos-initiative.org/wp-content/uploads/2021/10/Keoghetal-2020.pdf), [latent](https://mc-stan.org/docs/stan-users-guide/measurement-error.html).

statistics

Oct 26, 2026

20 questions

Quiz

Multiple Imputation & Sensitivity Analysis

Develop practical judgment in multiple imputation & sensitivity analysis through 20 original single-select and multiselect problems. Practice Rubin pooling, compatible imputation models, design-aware inference, fold-safe prediction and explicit MNAR sensitivity analysis. Begin with concepts, then work through calculations, diagnostic scenarios and assumption-sensitive challenges. Prerequisites: probability, regression, statistical inference and the preceding syllabus topics. Detailed worked solutions explain the reasoning and each distractor. Intended easy-to-hard order is retained in the course record; app attempts may reorder questions. Cover: Photo by Lukas Blazek on Pexels. Further reading: [Rubin pooling](https://stefvanbuuren.name/fimd/sec-whyandwhen.html), [support](https://stefvanbuuren.name/fimd/sec-pmm.html), [design](https://stefvanbuuren.name/fimd/sec-nonresponse.html), [pool](https://stefvanbuuren.name/fimd/sec-pooling.html).

statistics

Oct 26, 2026

20 questions

Quiz

Missingness Mechanisms & Complete-Case Analysis

Develop practical judgment in missingness mechanisms & complete-case analysis through 20 original single-select and multiselect problems. Study MCAR, MAR and MNAR relative to observed information, estimand-dependent complete-case validity, selection, and the uncertainty hidden by simple fills. Begin with concepts, then work through calculations, diagnostic scenarios and assumption-sensitive challenges. Prerequisites: probability, regression, statistical inference and the preceding syllabus topics. Detailed worked solutions explain the reasoning and each distractor. Intended easy-to-hard order is retained in the course record; app attempts may reorder questions. Cover: Photo by Lukas Blazek on Pexels. Further reading: [mechanism](https://stefvanbuuren.name/fimd/sec-MCAR.html), [deletion](https://stefvanbuuren.name/fimd/sec-simplesolutions.html), [dag](https://mixtape.scunning.com/03-directed_acyclical_graphs).

statistics

Oct 26, 2026

20 questions

Quiz

Bayesian Hierarchical Regression & Predictive Checks

Develop practical judgment in bayesian hierarchical regression & predictive checks through 20 original single-select and multiselect problems. Focus on group-level priors, equivalent parameterizations, preserved dependence in replication, and prediction for observed versus unseen groups. Begin with concepts, then work through calculations, diagnostic scenarios and assumption-sensitive challenges. Prerequisites: probability, regression, statistical inference and the preceding syllabus topics. Detailed worked solutions explain the reasoning and each distractor. Intended easy-to-hard order is retained in the course record; app attempts may reorder questions. Cover: Photo by Vitaly Gariev on Pexels. Further reading: [geometry](https://mc-stan.org/docs/stan-users-guide/efficiency-tuning.html), [predict](https://mc-stan.org/docs/stan-users-guide/posterior-predictive-checks.html).

statistics

Oct 26, 2026

20 questions

Quiz

Longitudinal Models, GLMMs & GEE

Model repeated and non-Gaussian outcomes while keeping the inferential target clear. Compare subject-specific GLMMs with population-average GEE, interpret logistic and count models, and assess working correlations and sandwich covariance. Prerequisites: generalized linear models, random effects and clustered sampling. Longitudinal examples address time interactions, within-person associations, missingness, few clusters and individual prediction. Single-select and multiselect practice includes worked explanations and distractor rationales. Study sources: statsmodels: Generalized Estimating Equations; Bayesian Data Analysis, Chapters 16 and 18. Photo by Kampus Production on Pexels.

statistics

Oct 26, 2026

20 questions

Quiz

Random Slopes, Crossed Effects & Estimation

Extend hierarchical models to varying trajectories and crossed measurements. Practice random-slope covariance, time centering, covariance admissibility and the distinction between nested and crossed designs. Prerequisites: random intercepts, matrix covariance, linear regression and likelihood. Examine ML versus REML comparisons, singular fits, identifiability, boundary tests and the effects of covariance constraints. Single-select and multiselect practice includes worked explanations and distractor rationales. Study sources: lme4: Singularity; lme4: Model Comparisons. Photo by Alena Darmel on Pexels.

statistics

Oct 26, 2026

20 questions

Quiz

Random Intercepts, Partial Pooling & ICC

Understand how hierarchical models share information across groups. Calculate random-intercept covariance, intraclass correlation, group-average precision and shrinkage weights, then assess their assumptions. Prerequisites: linear regression, variance decomposition and Normal distributions. School, hospital and prediction examples highlight exchangeability, group-level confounding, limited cluster counts and existing versus new groups. Single-select and multiselect practice includes worked explanations and distractor rationales. Study sources: Bayesian Data Analysis, Chapters 5 and 15; statsmodels: Linear Mixed Effects Models. Photo by cottonbro studio on Pexels.

statistics

Oct 26, 2026

20 questions

Quiz

Statistical Decision Theory & Loss Functions

Turn statistical uncertainty into decisions through explicit consequences. Practice posterior expected loss, frequentist and Bayes risks, constrained actions, asymmetric costs, minimax rules and dominance. Prerequisites: posterior expectation, probability distributions and elementary optimization. Advanced challenges address continuous zero-one loss, finite tolerance, randomized rules and the expected value of information. Single-select and multiselect practice includes worked explanations and distractor rationales. Study sources: University of Washington: Elementary Decision Theory. Photo by Francie on Pexels.

statistics

Oct 26, 2026

20 questions

Quiz

Bayesian Hypothesis Testing & Bayes Factors

Compare Bayesian models using integrated evidence and explicit prior odds. Practice Bayes factors, discrete marginal-likelihood calculations, sequential updating and model-probability normalization. Prerequisites: Bayes updating, likelihoods and integration. Examine evidence conventions, diffuse-prior sensitivity, Lindley behavior, improper priors and numerical reliability, with careful distinctions from classical p-values. Single-select and multiselect practice includes worked explanations and distractor rationales. Study sources: Bayesian Data Analysis, Section 7.4; The Book of Statistical Proofs: Bayes Factor. Photo by Tiger Lily on Pexels.

statistics

Oct 26, 2026

25 questions

Quiz

Prior & Posterior Predictive Checking

Use simulated data to examine the implications of Bayesian models. Practice prior and posterior predictive simulation, targeted discrepancy selection, variance decomposition, hierarchical replication and sensitivity analysis. Prerequisites: posterior simulation, conditional expectation, basic count models and introductory hierarchical modeling. Scenarios distinguish model checking, predictive validation and individual-outcome uncertainty. Single-select and multiselect practice includes worked explanations and distractor rationales. Study sources: Stan: Prior and Posterior Predictive Checks. Photo by Murilo Papini on Pexels.

statistics

Oct 26, 2026

20 questions