*Power*

## David C. Howell

Most of what I currently have on Power comes from software
programs that generate samples, compute statistics, and report those statistics. I'll add
other kinds of things as I come to them.

- The power of chi-square can be demonstrated in the three-door
problem.
- The power of
*t* tests can be seen with this
linked program.
- A particularly interesting example can be seen with the
*F*
test. Here you have the option of changing several different parameters to
examine their effects on power.
- Power when you aren't satisfied to just reject the null.
This is a document that challenges how we think about power when we are working in fields
in which the mere reject of a strict null hypothesis is not sufficient.It raises some
interesting questions, and does not present definitive answers. If I were one of my
graduate students, I'd think about this one before the next exam on power.
- A neat, clean, and free program to calculate power for several
selected tests. It offers instructors a good way to illustrate factors controlling power,
and provides students the opportunity to play around with power calculations. I expect
that the authors will continue to add test statistics to their list for which power can be
calculated.
- A lab exercise investigating power (as well as
*t* and
correlation) is useful. It assumes SPSS, but the program could be rewritten for a
different software package.
- A great example about the practical implications of power analysis can be
found at Utts-Salaries.html.Utts presents
an example of a situation where, because of low power, an nonsignificant
difference can still be important.

Return to
Dave Howell's Statistical Home Page

University of Vermont Home Page

Last revised: 7/11/98