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
There are now several variations of the original LSR book:
I have suggested that someone write a Learning Statistics with an
Abacus adaptation but so far there has been little interest.
Table of Contents
- 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
II. An introduction to R
- 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.
III. Working with data
- 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
- 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.
IV. Statistical theory
- 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
- 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
VI. Other topics
- 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.