Back in the grimdark pre-Snapchat era of humanity (i.e. early 2011), I started teaching an introductory statistics class for psychology students offered at the University of Adelaide, using the R statistical package as the primary tool. I wrote my own lecture notes for the class, which have now expanded to the point of effectively being a book. The book is freely available, and as of version 0.6 it is released under a creative commons licence (CC BY-SA 4.0)

The book is associated with the `lsr`

package on CRAN and
GitHub. The package is probably okay for many introductory teaching
purposes, but some care is required. The package does have some
limitations (e.g., the `etaSquared`

function does strange
things for unbalanced ANOVA designs), and it has not been updated in a
while.

There are now several variations of the original LSR book:

- Emily Kothe created a bookdown adaptation of LSR
- Matt Crump has incorporated some LSR content in Answering Questions with Data
- My own R programming notes R for Psychological Science adapt some LSR content
- David Foxcroft has adapted LSR to create Learning Statistics with Jamovi
- Tom Faulkenberry has adapted David Foxcroft’s version to create Learning Statistics with JASP
- Jean-Marc Meunier has translated
*Learning Statistics with Jamovi*into French - Ethan Weed has started work on a Learning Statistics with Python adaptation (this is a work in progress!)
- Róbert Fodor is working on Learning Statistics with Cogstat

I have suggested that someone write a *Learning Statistics with an
Abacus* adaptation but so far there has been little interest.

**Chapter 1: Why do we learn statistics?**Psychology and statistics. Statistics in everyday life. Some examples where intuition is misleading, and statistics is critical.**Chapter 2: A brief introduction to research design.**Basics of psychological measurement. Reliability and validity of a measurement. Experimental and non-experimental design. Predictors versus outcomes.

**Chapter 3: Getting started with R.**Getting R and Rstudio. Typing commands at the console. Simple calculations. Using functions. Introduction to variables. Numeric, character and logical data. Storing multiple values as a vector.**Chapter 4: Additional R concepts.**Installing and loading packages. The workspace. Navigating the file system. More complicated data structures: factors, data frames, lists and formulas. A brief discussion of generic functions.

**Chapter 5: Descriptive statistics.**Mean, median and mode. Range, interquartile range and standard deviations. Skew and kurtosis. Standard scores. Correlations. Tools for computing these things in R. Brief comments missing data.**Chapter 6: Drawing graphs.**Discussion of R graphics. Histograms. Stem and leaf plots. Boxplots. Scatterplots. Bar graphs.**Chapter 7: Pragmatic matters.**Tabulating data. Transforming a variable. Subsetting vectors and data frames. Sorting, transposing and merging data. Reshaping a data frame. Basics of text processing. Reading unusual data files. Basics of variable coercion. Even more data structures. Other miscellaneous topics, including floating point arithmetic.**Chapter 8: Basic programming.**Scripts. Loops. Conditionals. Writing functions. Implicit loops.

**Prelude.**The riddle of induction, and why statisticians make assumptions.**Chapter 9: Introduction to probability.**Probability versus statistics. Basics of probability theory. Common distributions: normal, binomial, t, chi-square, F. Bayesian versus frequentist probability.**Chapter 10: Estimating unknown quantities from a sample.**Sampling from populations. Estimating population means and standard deviations. Sampling distributions. The central limit theorem. Confidence intervals.**Chapter 11: Hypothesis testing.**Research hypotheses versus statistical hypotheses. Null versus alternative hypotheses. Type I and Type II errors. Sampling distributions for test statistics. Hypothesis testing as decision making. p-values. Reporting the results of a test. Effect size and power. Controversies and traps in hypothesis testing.

**Chapter 12: Categorical data analysis.**Chi-square goodness of fit test. Chi-square test of independence. Yate’s continuity correction. Effect size with Cramer’s V. Assumptions of the tests. Other tests: Fisher exact test and McNemar’s test.**Chapter 13: Comparing two means.**One sample z-test. One sample t-test. Student’s independent sample t-test. Welch’s independent samples t-test. Paired sample t-test. Effect size with Cohen’s d. Checking the normality assumption. Wilcoxon tests for non-normal data.**Chapter 14: Comparing several means (one-way ANOVA).**Introduction to one-way ANOVA. Doing it in R. Effect size with eta-squared. Simple corrections for multiple comparisons (post hoc tests). Assumptions of one-way ANOVA. Checking homogeneity of variance using Levene tests. Avoiding the homogeneity of variance assumption. Checking and avoiding the normality assumption. Relationship between ANOVA and t-tests.**Chapter 15: Linear regression.**Introduction to regression. Estimation by least squares. Multiple regression models. Measuring the fit of a regression model. Hypothesis tests for regression models. Standardised regression coefficient. Assumptions of regression models. Basic regression diagnostics. Model selection methods for regression.**Chapter 16: Factorial ANOVA.**Factorial ANOVA without interactions. Factorial ANOVA with interactions. Effect sizes, estimated marginal means, confidence intervals for effects. Assumption checking. F-tests as model selection. Interpreting ANOVA as a linear model. Specifying contrasts. Post hoc testing via Tukey’s HSD. Factorial ANOVA with unbalanced data (Type I, III and III sums of squares)

**Chapter 17: Bayesian statistics.**Introduction to Bayesian inference. Bayesian analysis of contingency tables. Bayesian t-tests, ANOVAs and regressions.**Chapter 18: Epilogue.**Comments on the content missing from this book. Advantages to using R.**References.**An incomplete reference list.