Lectures
Lecture slide decks are available in html format.
Lecture 1
Statistics and Samples
Introduction to populations, samples, and the role of sampling in statistical inference.
Lecture 2
Data and Variables
Types of variables, their roles in analysis, and why correlation does not imply causation.
Lecture 3
Effective data visualization
Key principles of good graphics, focusing on clarity, honesty, and making patterns easy to see.
Lecture 4
Visualizing Data Types
Common visualization types and their suitability for different data types and analytical goals.
Lecture 5
Describing Data
Describing data using measures of location, variability, and appropriate numerical precision.
Lecture 6
Estimating with uncertainty
Estimating population parameters and their uncertainty from sample data
Lecture 7
Probability
Introduces probability theory as the foundation of statistical reasoning, focusing on conditional probability, independence, and Bayes’ theorem.
Lecture 8
Hypothesis testing
An introduction to hypothesis testing in biology using a proportion example to illustrate null models, \(P\)-values, error types, and scientific interpretation.
Lecture 9
Analyzing proportions
Introduces the binomial distribution for modeling binary outcomes and develops the statistical tools needed to estimate and draw inference about population proportions.
Lecture 10
Fitting Probability Models to Frequency Data
Using proportional probability models and the chi-squared goodness-of-fit test to evaluate whether observed categorical frequency data fit expectations under a null hypothesis.
Lecture 11
Using the Poisson Distribution to Test Randomness in Count Data
Introduces the Poisson distribution as a model for random counts in time or space and shows how to test whether observed data fit this model using a chi-square goodness-of-fit test.
Lecture 12
Contingency Analysis
Introduces methods for exploring the association between two categorical variables using contingency tables. We develop conditional probability (risk), relative risk, odds, the odds ratio, and the chi-squared test for independence.
Lecture 13
The Normal Distribution
Introduces the normal distribution, its properties, and how Z-scores are used to calculate probabilities. Also explains the sampling distribution of the mean and how sample size affects its spread.
Lecture 14
Inference for Population Means
Confidence Intervals and t-Tests
Introduces statistical inference for normally distributed data, including confidence intervals for the mean and hypothesis tests (one-sample, independent two-sample, and paired t-tests) used to evaluate differences in population means.
Lecture 15
Comparing Two Means
Introduces statistical methods for comparing two means, including Welch two-sample and paired t-tests, confidence intervals for mean differences, and appropriate study designs for two-sample inference.
Lecture 16
Handling Violations of Assumptions
Handling violations of statistical assumptions, including when to proceed, transform data, or use alternative methods for non-normal data.
Lecture 17
Choosing a test
Summarizes the decision process for choosing an appropriate statistical test based on the research question, data types, and study design. Provides a flowchart to guide test selection.
Lecture 18
Designing Experiments
Introduces how well-designed experiments enable causal inference by reducing bias and sampling error, while contrasting their strengths with observational studies and outlining practical strategies for study design and sample size planning
Lecture 19
Comparing means of more than two groups
Introduces Analysis of Variance (ANOVA) as a method for comparing means across multiple groups by partitioning variation and testing whether between-group differences exceed within-group variation.