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Stats Power. Report Confidence Limits - p values

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  • Stats Power. Report Confidence Limits - p values

    Just did a quick search and found this:

    So it looks like the way to go is Monte Carlo simulation


    Gordon Chalmers wrote:

    > An interesting review of the history of the p-value, and its potential
    > role today can be found in a BMJ article freely available at:
    > Sifting the evidence-what's wrong with significance tests?
    > Jonathan A C Sterne, George Davey Smith
    > In the introduction it states: "In this paper we consider how the
    > practice of significance testing emerged; an arbitrary division of
    > results as "significant" or "non-significant" (according to the commonly
    > used threshold of P=0.05) was not the intention of the founders of
    > statistical inference. P values need to be much smaller than 0.05 before
    > they can be considered to provide strong evidence against the null
    > hypothesis; this implies that more powerful studies are needed.
    > Reporting of medical research should continue to move from the idea that
    > results are significant or non-significant to the interpretation of
    > findings in the context of the type of study and other available
    > evidence."
    > ************************************************** ******************
    > Gordon Chalmers, Ph.D.
    > Dept. of Physical Education, Health and Recreation
    > Western Washington University
    > 516 High St.
    > Bellingham, WA, U.S.A.
    > 98225-9067
    > Phone: 360-650-3113
    > Email: Gordon-dot-Chalmers-at-wwu-dot-edu
    > in above email address: replace "-dot-" with "."
    > replace "-at-" with "@"
    > -----Original Message-----
    > From: * Biomechanics and Movement Science listserver
    > [mailto:BIOMCH-L@NIC.SURFNET.NL] On Behalf Of Bryan Kirking
    > Sent: Tuesday, January 25, 2005 2:46 PM
    > Subject: Re: [BIOMCH-L] Stats Power. Report Confidence Limits - p values
    > To comment and question some of Dr. Allison's insight:
    > >>My understanding of the arbitrary "line in the sand" of 0.05 was
    > >>originally due to the choice of the original tables (pre computer)
    > I have heard this too. It was very tedious to calculate probabilities
    > (pre
    > Personal Computer) as is done now, so the investigator would pick the
    > appropriate values to simplify the calculations.
    > >>The p value reflects the probability of the observed change happening
    > by
    > chance.
    > Isn't this only correct if the null hypothesis is correct (not
    > rejected?). This is why (as explained to me by statisticians - I won't
    > claim authority here) it is considered incorrect to differentiate
    > "significant" from "very significant" from "highly significant"? I
    > present
    > this point because of your comment about relating the alpha level to the
    > seriousness of the outcome.
    > Bryan Kirking
    > ProbaSci LLC
    > tel. 512.218.3900
    > fax. 512.218.3972
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    Dr. Chris Kirtley MD PhD
    Associate Professor
    Dept. of Biomedical Engineering
    Catholic University of America
    Washington DC 20064
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