Significance Testing

Colin Kaneen ckaneen at HOME.COM
Thu Jun 14 15:51:37 CDT 2001


There are some reasons for using the methods you are talking about (keep in
mind while reading this my background only goes as far as second year
undergrad statistics):

>Why do we use significance (P values like: P>.01) ?
>What does it really tell us that using confidence intervals don't?

The p-value is used in hypothesis testing.  It tells us "the smallest
significance level at which the null hypotheis can be rejected" (Weiss,
1995 _Introductory Stataistics_, 4th ed.).  A confidence interval gives us
the end-point values for a given singificance test.  In science we tend to
use p>.05

>Why, in biological papers mainly having to do with experiments, don't we
>incorporate a consideration for "power", i.e. calculate for sample size and
>parameter range?

Sometimes power is considered, but it may depend on the type of test
performed.   If the test is a z- or t-test, power is equal to 1-beta, where
beta is 1-alpha.  In other types of tests however, such as chi-squares,
power is not calculated this way.  As I understand, the calculation of
power is quite complex and is beyond the scope of my knowledge.
>At what point is a sample size "good"?
I don't know what you mean by "good."  If you mean how many points are
needed for a sample to be useful, that depends on what is being sampled.
Normally 30 with a non-normal population is large enough to allow us to use
normal statistical tests rather than resorting to non-parametric tests such
as the sign or Wilcoxon Signed Rank tests.

Does this help?

Colin Kaneen

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