Test whether a distribution is normal — effect_metrics

The test is calculated using stats::shapiro.test.

Usage

effect_metrics_one(data, col, labels = TRUE, clean = TRUE, ...)

Arguments

data: A tibble.
col: The column holding metric values.
labels: If TRUE (default) extracts labels from the attributes, see codebook.
clean: Prepare data by data_clean.
...: Placeholder to allow calling the method with unused parameters from effect_metrics.

Value

A volker list object with the following statistical measures:

skewness: Measure of asymmetry in the distribution. A value of 0 indicates perfect symmetry.
kurtosis: Measure of the "tailedness" of the distribution.
W: W-statistic from the Shapiro-Wilk normality test.
p: p-value for the statistical test.
stars: Significance stars based on p-value (*, **, ***).
normality: Interpretation of normality based on Shapiro-Wilk test.

Examples

library(volker)
data <- volker::chatgpt

effect_metrics_one(data, sd_age)
#> 
#> 
#> |Age      | Value|
#> |:--------|-----:|
#> |skewness |  0.25|
#> |kurtosis | -0.98|
#> 
#> 
#> |Shapiro-Wilk normality test |      Value|
#> |:---------------------------|----------:|
#> |W                           |       0.96|
#> |p                           |      0.002|
#> |stars                       |         **|
#> |normality                   | not normal|