Yet Another Statistics-for-Linguistics Book
2026/10/27 DRAFT
James Myers
National Chung Cheng University, Graduate Institute of Linguistics
Special features of this book:
- Teaches you how to use the free and powerful R software,
popular among linguists because they're cheap
- Also teaches you how to use Excel, because, let's face it, R can get kind of intimidating
- Lots of terms are translated into Chinese, in case your Chinese is better than your English
- A healthy mix of messy real data sets and nice and clean fake data sets, involving language
teaching, psycholinguistics, phonetics, corpus analysis, typology, child language, and whatever else
I had handy
- Mixed-effects modeling demystified (and guidance in navigating the continuing debates)
- Some but not much ggplot2 or tidyverse, but most people end up copy/pasting examples from the web anyway
- Weird-but-cool stuff like resampling, generalized additive modeling, and Bayesian statistics
- Advice on how to look up stuff yourself, because you're an adult, right?
- Lots of cleverly hidden typos to challenge your critical thinking skills
- Basic biographical info on statisticians (mostly dead white men)
- An update schedule that's about what you'd expect for something done for free in my spare time
- An irritating "humorous" writing style similar to that used in this list
Chapter 1: Why do linguists need statistics? [2026/8/25]
Chapter 2: Data analysis software: Excel and R [2026/8/25]
Chapter 3: Quantifying some familiar ideas: Averages and variation [2026/9/10]
Chapter 4: Probability and hypotheses [2026/9/17]
Chapter 5: Correlation and modeling [2026/9/23]
Chapter 6: Comparing two continuous variables: t tests and beyond [2026/10/11]
Chapter 7: Comparing category sizes: Chi-squared and related tests [2026/10/21]
Chapter 8: Comparing more than two continuous variables:
Introduction to ANOVA [2026/10/27]
Chapter 9: More ANOVA: Repeated measures [2026/10/27]
Chapter XXX: Data exploration: Cluster analysis and related methods [not written yet]
Chapter 10: Modeling continuous variables: Multiple regression [2026/10/27]
Chapter 11: Modeling categorical variables: Logistic regression [2026/10/27]
Chapter 12: Beyond ANOVA, regression, and chi-squared:
Mixed-effects modeling [2026/10/27]
Chapter 13: The future of statistics: Bayesian modeling [2022/5/14]
Statistics resources (including data files)