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Biostatistics with Python33

01Translating a Research Question into Variables 02Distinguishing Observation Units, Experimental Units, and Replicates 03Data Types, Schema, and Provenance 04Summarizing Center, Spread, and Distribution 05Distribution Visualization and Outliers 06Judging Missing Values, Duplicates, and Transformations 07Probability Models and the Data Generating Process 08Sampling Distribution and Standard Error 09Confidence Intervals, Bootstrap, and Permutation 10Effect Size, Uncertainty, and p-value 11Two-Group Paired Nonparametric Comparison 12ANOVA · Multiple Comparisons · FDR 13Power and Sample Size Planning 14Randomization, Blocking, and Matching 15Factorial Designs and Interactions 16Repeated Measures, Clustering, and Pseudoreplication 17OLS, Covariates, and Diagnostics 18Logistic Regression 19Poisson, offset, and negative binomial 20Mixed Model Basics 21Longitudinal Change and Missingness 22Censoring, Kaplan–Meier, and Risk Sets 23Cox Model and the Proportional Hazards Assumption 24Count Matrices, Normalization, and Batch Effects 25Compositional Data and Log-Ratios 26PCA and High-Dimensional QC 27Multiple Testing and Shrinkage 28Prediction Target, Baseline, and Metric 29Leakage, Pipeline, and Group/Time/Site Split 30Feature Selection and Nested Tuning 31Calibration, Class Imbalance, and Uncertainty 32Reproducible Analysis Package 33Communicating Results, Limitations, Ethics, and Accessibility

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