test.kurtosis

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)

Implements confidence interval and sample size methods that are especially
useful in psychological research but are also useful in educational, social science,
business, and biological research. This package includes more than 100 confidence
interval functions and more than 80 sample size functions for 1-group, 2-group,
paired-samples, and multiple-group designs and for a variety of parameters including
means, medians, proportions, slopes, standardized mean differences, standardized
linear contrasts of means, and several measures of correlation and association. The
sample size functions can be used to approximate the sample size needed to estimate
a parameter or function of parameters with desired confidence interval precision or
to perform a variety of hypothesis tests (directional two-sided, equivalence,
superiority, noninferiority) with desired power. For details about these methods see:
Statistical Methods for Psychologists, Volumes 1 – 4, <https://dgbonett.sites.ucsc.edu/>.

Douglas G. Bonett

statpsych

Statistical Methods for Psychologists

Robert J. Calin-Jageman

test.kurtosis function

<dl><dt>y</dt>
<dd>vector of quantitative scores</dd></dl>

Arguments

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

<dl>

<dt>y</dt>
<dd>vector of quantitative scores</dd>

</dl>

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

Description

Usage

Value

Arguments

References

Examples