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FW: [BIOMCH-L] Stats Power. Report Confidence Limits - p values

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  • FW: [BIOMCH-L] Stats Power. Report Confidence Limits - p values

    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:

    http://bmj.com/cgi/reprint/322/7280/226

    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
    http://www.ac.wwu.edu/~chalmers/
    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
    To: BIOMCH-L@NIC.SURFNET.NL
    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
    www.probasci.com
    bryan@probasci.com

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