# RE: st: RE: P-value

 From Roger Newson To statalist@hsphsun2.harvard.edu Subject RE: st: RE: P-value Date Fri, 11 Nov 2005 18:19:36 +0000

If Raphael wants to see P-values to a greater precision than 3 decimal places, then Raphael might like to use the -parmest- package, downloadable from SSC. For instance, if you install -parmest-, and type

regress y x1 x2 x3
parmest, list(parm estimate min* max* p) format(p %-8.2g)

then the P-values will be displayed to 2 significant figures, rather than to 3 decimal places. Raphael will therefore know whether a P-value of 0.000 means 4*10^-4, 4*10^-5 or 4*10^-9. This distinction might be important if you have calculated a lot of P-values, and want to know whether the smallest ones are credible, given the number of P-values that you have calculated. After all, 5 percent of P-values will be significant at the 5 percent level, even if all null hypotheses are true. On the other hand, if all null hypotheses are true, then you would have to calculate a very large number of P-values to find a P-value of 4*10^-9 just by chance.

A possible alternative to -parmest- is -estout-, which is also downloadable from SSC and does a lot of similar things.

I hope this helps.

Roger

At 17:27 11/11/2005, you wrote:

>-----Original Message-----
>From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu]On Behalf Of Raphael Fraser
>Sent: vrijdag 11 november 2005 18:17
>To: statalist@hsphsun2.harvard.edu
>Subject: Re: st: RE: P-value
>
>That's what I think too but I keep seeing people interpreting 0.000 as
><0.0001. Am I missing something here?

very small is very small, your null hypothesis is rejected by any conventional confidence level, which is all that P-values are supposed to say.

>
>>On 11/11/05, Maarten Buis <M.Buis@fsw.vu.nl> wrote:
>> as very small (less than 0.0005)

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Roger Newson
Lecturer in Medical Statistics
Department of Public Health Sciences
Division of Asthma, Allergy and Lung Biology
King's College London

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