Re: [R] shapiro wilk normality test

From: Johannes Huesing <>
Date: Sun, 13 Jul 2008 23:42:19 +0200

Ted Harding <> [Sun, Jul 13, 2008 at 10:59:21PM CEST]:
> On 13-Jul-08 19:53:47, Johannes Huesing wrote:
> > Frank E Harrell Jr <> [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  from such a trifling investment of fact.                (Mark Twain, "Life on the Mississippi")

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Received on Sun 13 Jul 2008 - 22:12:38 GMT

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