[R] shapiro wilk normality test

[Johannes Huesing]

Ted Harding <Ted.Harding at manchester.ac.uk> [Sun, Jul 13, 2008 at 10:59:21PM CEST]:
> On 13-Jul-08 19:53:47, Johannes Huesing wrote:
> > Frank E Harrell Jr <f.harrell at vanderbilt.edu> [Sun, Jul 13, 2008 at
> > 08:07:37PM CEST]:
> >> (Ted Harding) wrote:
> >>> On 13-Jul-08 13:29:13, Frank E Harrell Jr wrote:
> >>>> [...]
> >>>> A large P-value means nothing more than needing more data.  No  
> >>>> conclusion is possible.  
[...]

> But "absence
> of evidence", in my interpretation (which I believe is right for
> the statistical context of "non-significant P-values"), means that
> we do not know about A: we do not have enough information.
> 

What would the p-value have to be like in your opinion to make the
null hypothesis look more likely after the experiment than before?

> The proof is, basically, given in terms of a 2-valued logic where
> every term is either TRUE or FALSE. In the real world we have at
> least a third possible value: UNKNOWN (or, as R would put it, NA).

How would the probabilities that A is NA be affected by the outcome
of an experiment like this? If this probability is affected, how
does this leave the probability that A is T or F unaffected?

Or do you assign the NA status to the data collected?

A high p-value does not always equate that you might as well have 
collected nothing but missing values. 

Of course I buy into the notion that a point estimate with a measure
of accuracy is much better suited to describe your data; but a
high p-value as a result of a test procedure that can be claimed to
be adequately powered may defensibly be taken as a hint that we
can for now stick with the null hypothesis.
-- 
Johannes Hüsing               There is something fascinating about science. 
                              One gets such wholesale returns of conjecture 
mailto:johannes at huesing.name  from such a trifling investment of fact.                
http://derwisch.wikidot.com         (Mark Twain, "Life on the Mississippi")