Normality and distribution checks
What "assumes normality" actually refers to, and the fact that it is usually the residuals and not the raw variable.
Slides · Dataset
About 30 minutes to complete
Enrol to open the slides and download the data.
What this module covers
what "assumes normality" actually refers to, and the fact that it is usually the residuals and not the raw variable; histogram, Q-Q plot and box plot read together; Shapiro-Wilk and Kolmogorov-Smirnov, and why both become useless at large N; skewness and kurtosis thresholds; the central limit theorem and when it rescues you; transformation as an option and its cost to interpretation; the decision rule for parametric vs non-parametric.
By the end of it
- What "assumes normality" actually refers to, and the fact that it is usually the residuals and not the raw variable
- Histogram, Q-Q plot and box plot read together
- Shapiro-Wilk and Kolmogorov-Smirnov, and why both become useless at large N
- Skewness and kurtosis thresholds
- The central limit theorem and when it rescues you
- Transformation as an option and its cost to interpretation
- The decision rule for parametric vs non-parametric
The dataset
Antenatal care and birth outcome — Sigma teaching dataset
A teaching dataset of 300 cases, generated from a fixed seed so every figure in the module reproduces exactly.
- CodebookIncluded
- Dataset (CSV)Included
- Dataset (CSV)Included
- SPSS syntaxIncluded