Introduction
Week 1: Statistical Foundations
Day 1: Why Statistics? The Story Your Data Is Trying to Tell
Day 2: Your Data at a Glance — Descriptive Statistics
Day 3: Distributions — The Shape of Biological Variation
Day 4: Probability — Quantifying Uncertainty
Day 5: Sampling, Bias, and Why n Matters
Week 2: Core Statistical Methods
Day 6: Confidence Intervals — The Range of Truth
Day 7: Hypothesis Testing — Asking Precise Questions
Day 8: Comparing Two Groups — The t-Test
Day 9: When Normality Fails — Non-Parametric Tests
Day 10: Comparing Many Groups — ANOVA and Beyond
Day 11: Categorical Data — Chi-Square and Fisher's Exact
Day 12: The Multiple Testing Crisis — FDR and Correction
Week 3: Modeling and Applied Biostatistics
Day 13: Correlation — Finding Relationships
Day 14: Linear Regression — Prediction from Data
Day 15: Multiple Regression and Model Selection
Day 16: Logistic Regression — Binary Outcomes
Day 17: Survival Analysis — Time-to-Event Data
Day 18: Experimental Design and Statistical Power
Day 19: Effect Sizes — Beyond p-Values
Day 20: Batch Effects and Confounders
Week 4: Advanced Methods and Capstone Projects
Day 21: Dimensionality Reduction — PCA and Friends
Day 22: Clustering — Finding Structure in Omics Data
Day 23: Resampling — Bootstrap and Permutation Tests
Day 24: Bayesian Thinking for Biologists
Day 25: Statistical Visualization — Plots That Tell the Truth
Day 26: Meta-Analysis — Combining Studies
Day 27: Reproducible Statistical Analysis
Day 28: Capstone — Clinical Trial Analysis
Day 29: Capstone — Differential Expression Study
Day 30: Capstone — Genome-Wide Association Study
Appendices
Appendix A: Installation and Setup
Appendix B: Statistical Decision Flowchart
Appendix C: Distribution Reference Card
Appendix D: Glossary
Appendix E: BioLang Statistics Quick Reference