|Date: ||Fri, 6 Aug 2010 16:54:52 -0400|
|Reply-To: ||Jim Groeneveld <jim.1stat@YAHOO.COM>|
|Sender: ||"SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>|
|From: ||Jim Groeneveld <jim.1stat@YAHOO.COM>|
|Subject: ||Re: Significant vs Non-Significant Effects|
What does significance mean and how or rather when do you investigate it?
First of all the borderline of significance is usually set to 95%. What does
that mean? It means that your __preceding__ hypothesis stating any
difference has a probability of at least 95% to be true in reality; that is
a generally accepted probability.
The 95% assurance does not mean that there is a difference, or that the
difference occurs in at least 95% of the cases. It only means that your
conclusion on accepting a difference in reality has a minimally 95% chance
to be true and a 5% (or less) chance of being false.
You have a __preceding__ hypothesis, don't you? Without a __preceding__
hypothesis there is no significance testing. The hypotheses can be general,
global (like a full ANOVA design) or detailed (concerning specific
difference between certain groups or so) or both.
Before testing you determine what to test and in what order. You __never__
deviate from that plan, agreed? If you deviate your conclusions are not
valid, but manipulative. Significance testing is not exploratory research,
you can't search for "significance".
Now, let's assume you had a hypothesis stating that the overall design would
show a difference with at least a 95% chance to be really true, thus
significance and that you also have a hypothesis stating that a certain
detail effect would be significant in the same way.
If you would plan to test those hypotheses in that order and if you agree to
stop testing any further once the overall effect would not show to be
significant, than you should stop indeed. If you yet continued to test
detail effects, that apparently turn out "significant", that significance
has no value. You would fool yourself by thinking that your conclusion on
the detail effects have a chance of at least 95% to be really true.
The only thing that you could do is, when you find detail "significance", to
revise your hypotheses and test those with completely new data. Significance
testing does not test differences or other effects (like equivalence) but it
only tests hypotheses, initial expectations, verdicts on those differences
or effects. And there is always the (generally accepted) small (<5%) chance
of still being wrong with your conclusions.
Statistics is not magic, no hocus-pocus, it never 'proves' anything 100%
sure, it only shows its likeliness. So I would stick to the rules of
statistics and the preceding test plan and I would not fiddle around until
finding something "significant". If you search long enough you can by chance
always find something "significant" (e.g. in 5% of your tests) without being
true actually, purely a coincidental finding without any significance, any
Beware! I hope these warnings are clarifying.
Regards - Jim.
Jim Groeneveld, Netherlands
On Fri, 6 Aug 2010 13:15:31 -0400, Billy Thompson
>If there is a non-significant overall ANOVA you would not preform post hoc
>analysis on individual factors. However, let's say in the results you get
>a non-significant overall p value, yet you get a significant effect on one
>or more factors in the Type IV source table, wouldn't you consider these
>significant effects and perform post hoc on those factors found