Shapiro–Wilk test
The Shapiro–Wilk test is a statistical hypothesis test assessing whether a sample is consistent with a normal distribution.
Statistics Every Programmer Needs
The Shapiro–Wilk test is a statistical hypothesis test for whether a sample is consistent with a normal distribution.
- How it is used
- Uses formal normality testing to validate numeric data before analysis rather than relying solely on visual inspection.
- How deep it goes
- Defines the null hypothesis, applies a 5% decision rule, and demonstrates SciPy execution, but does not develop the test statistic formula.
- Examples
- Tests the stage1 and stage2 columns of a 20-row mds dataset using SciPy.
- What you can do after
- Evaluate normality with SciPy and interpret returned p-values against a 5% threshold.
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Statistics Slam Dunk
The Shapiro-Wilk test is a statistical test that evaluates whether a sample is consistent with a normal distribution.
- How it is used
- Screens variables for normality before correlation analysis and linear modeling.
- How deep it goes
- Defines the null hypothesis and cutoff, demonstrates R output, and stops short of deriving the test statistic.
- Examples
- Tests NBA hustle variables such as deflections, charges, wins, and points per game.
- What you can do after
- Diagnose distributional assumptions with base R and decide which variables remain regression candidates.
Related concepts
- shapiro.test() functionused by · The base R shapiro.test() function performs the Shapiro-Wilk test of whether numeric data are plausibly drawn from a normal distribution.
- p-valueproduces · A p-value is the probability, assuming a specified null hypothesis, of obtaining a test statistic or result at least as extreme as the observed one.
- histogramalternative to · A histogram is a representation that groups observations into bins or value ranges and records their frequencies or other distribution summaries.
- SciPyimplemented by · SciPy is an open-source Python library providing algorithms and numerical utilities for scientific and technical computing.
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