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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:

    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

    ************************************************** ******************
    Gordon Chalmers, Ph.D.
    Dept. of Physical Education, Health and Recreation
    Western Washington University
    516 High St.
    Bellingham, WA, U.S.A.
    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
    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

    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
    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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