test.kurtosis: Computes p-value for test of excess kurtosis

In statpsych: Statistical Methods for Psychologists

Computes p-value for test of excess kurtosis

Description

Computes a Monte Carlo p-value (250,000 replications) for the null hypothesis that the sample data come from a normal distribution. If the p-value is small (e.g., less than .05) and excess kurtosis is positive, then the normality assumption can be rejected due to leptokurtosis. If the p-value is small (e.g., less than .05) and excess kurtosis is negative, then the normality assumption can be rejected due to platykurtosis.

For more details, see Section 1.23 of Bonett (2021, Volume 1)

Usage

test.kurtosis(y)

Arguments

y	vector of quantitative scores

Value

Returns a 1-row matrix. The columns are:

• Kurtosis - estimate of kurtosis coefficient

• Excess - estimate of excess kurtosis (kurtosis - 3)

• p - Monte Carlo two-sided p-value

References

\insertRef

Bonett2021statpsych

Examples

y <- c(30, 20, 15, 10, 10, 60, 20, 25, 20, 30, 10, 5, 50, 40, 95)
test.kurtosis(y)

# Should return:
# Kurtosis  Excess     p
#   4.8149  1.8149 0.038 

statpsych documentation built on Jan. 13, 2026, 1:07 a.m.