The Basics:

• What is a statistical distribution?
• What is a statistical model?
• What does it mean to “sample from a distribution”?
• Standard Deviation vs Standard Error
• Pie vs. Bar Charts
• Logs (logarithms), clearly explained
• Confidence Intervals
• The Standard Error
• R-squared explained
• Fitting a line to data, aka Least Squares, aka Linear Regression
• Fitting a curve to data, aka Lowess, aka Loess
• General Linear Models Part 1: Linear Regression
• General Linear Models: Linear Regression in R
• General Linear Models Part 2: t-tests and ANOVA
• Maximum Likelihood
• Maximum Likelihood: A worked out example for the exponential distribution

Statistical Tests:

• Fisher’s Exact Test and Enrichment Analysis
• Which t-test to use
• p-values, clearly explained
• One or Two Tailed p-values
• Thresholds for Significance
• FDR and the Benjamini-Hochberg Method clearly explained
• p-hacking and power calculations

Dealing with large datasets that have lots and lots of measurements per sample:

• Linear Discriminant Analysis (LDA) clearly explained
• Principal Component Analysis (PCA) clearly explained
• t-SNE, clearly explained
• Heatmaps – considerations for drawing and interpreting them
• Hierarchical Clustering
• K-Nearest Neighbors

High-throughput Sequencing Analysis:

• A gentle introduction to RNA-seq
• edgeR, part1: Library Normalization
• DESeq2, part1: Library Normalization
• edgeR and DESeq2, part2: Independent Filtering (removing genes with low read counts)
• RNA-seq – the problem with technical replicates
• RPKM, FPKM, and TPM