- Explains the ideas of statistics without describing the mathematical underpinnings
- Appeals to the many students and scientists who prefer verbal explanations over mathematical proofs
- Focuses on how to avoid falling into common conceptual traps
- Points out ambiguities in potentially confusing terms and phrases
- Covers a wide breadth of topics in a quick and concise manner
- Can be used as a stand alone text or as a supplement to longer texts.
- Explains essential concepts missing from many books, including multiple comparisons, the False Discovery Rate (FDR), outliers and lognormal distributions.
Some ways in which this book is unique
I explain comparing groups with confidence intervals (Chapter 12) before
explaining P values (Chapter 13) and statistical significance (Chapters 14
and 15). This way I could delay as long as possible dealing with the confusing concept of a P value and the overused word “significant”.
Chapter 16 explains how common Type I errors are, and the difference
between a significance level and the false discovery rate.
Chapter 17 explains how multiple comparisons are an issue in almost all analyses. The issue of multiple comparisons goes way beyond followup tests after ANOVA.
Chapter 19 explains all common statistical tests as a series of tables.
I include topics often omitted from introductory texts, but that I consider
to be essential, including: the false discovery rate,
p-hacking, lognormal distributions, geometric mean, normality tests, outliers and nonlinear regression.
Nearly every chapter has a Lingo section that explains how statistical terminology can be misunderstood.
Nearly every chapter includes a Common Mistakes section, and Chapter 25 explains more general mistakes to avoid.