test.skew

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 the skewness estimate is
positive, then the normality assumption can be rejected due to positive
skewness. If the p-value is small (e.g., less than .05) and the skewness
estimate is negative, then the normality assumption can be rejected due
to negative skewness.

Implements confidence interval and sample size methods that
are especially useful in psychological research. The methods can be
applied in 1-group, 2-group, paired-samples, and multiple-group designs
and to a variety of parameters including means, medians, proportions,
slopes, standardized mean differences, standardized linear contrasts
of means, plus several measures of correlation and association.
Confidence interval and sample size functions are given for single
parameters as well as differences, ratios, and linear contrasts of
parameters. 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 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.skew function

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

Arguments

Computes p-value for test of skewness — test.skew

<dl>

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

</dl>

test.skew: Computes p-value for test of skewness

Description

Usage

Value

Arguments

Examples