# data analysis

bayes Bayesian data analysis, modeling, inference models Formulation, parameter estimation, and interpretation of specific statistical models generalizability Generalizability of studies and statistical inferences, sample representativeness, target population modeling strategy General model specification issues, nonlinearities, interactions and heterogeneity of treatment effect, avoiding categorization, how to sequence multiple steps (which may involve multiple imputation and data reduction) descriptive descriptive and exploratory data analysis, hypothesis generating more than confirmatory analysis probability Probability theory, meaning, and application exclusive of statistical tests, etc. formal statistical tests and inference data reduction data reduction (principal components, etc.), clustering, unsupervised learning model validation model validation and interpretation accuracy accuracy and information measures, discriminaton, calibration uncertainty Quantifying uncertainty, displaying uncertainty, estimation of uncertainty, incorporation of uncertainty into decision making, etc. This includes but is not limited to confidence intervals, standard errors, Bayesian credible intervals, and sources of uncertainty. data problems statistical approaches dealing with missing data and measurement error variable selection Selection of predictive features in multivariable modeling, one-at-a-time screening of variables, and the cost of feature selection compared to using fuller models, possibly with penalization (shrinkage; regularization). machine learning machine learning, exclusive of traditional statistical models comparative methods comparative performance of statistical analysis methods and predictive modeling approaches causal inference Methods and approaches to causal inference reporting This subcategory relates to how results of data analyses should be reported, for example which summary statistics should be reported for a logistic regression model.