power.mean

Computes the approximate power of a one-sample t-test for a planned sample
size. For a conservatively low power approximation, set the variance
planning value to the largest value within its plausible range, and set the
effect size to a minimally interesting value.

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

power.mean function

<dl><dt>alpha</dt>
<dd>alpha level for hypothesis test</dd>
<dt>n</dt>
<dd>planned sample size</dd>
<dt>var</dt>
<dd>planning value of response variable variance</dd>
<dt>es</dt>
<dd>planning value of mean minus null hypothesis value</dd></dl>

Arguments

Approximates the power of a one-sample t-test for a planned sample size — power.mean

<dl>

<dt>alpha</dt>
<dd>alpha level for hypothesis test</dd>


<dt>n</dt>
<dd>planned sample size</dd>


<dt>var</dt>
<dd>planning value of response variable variance</dd>


<dt>es</dt>
<dd>planning value of mean minus null hypothesis value</dd>

</dl>

power.mean: Approximates the power of a one-sample t-test for a planned sample size

Description

Usage

Value

Arguments

Examples