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Statiscal Difference Yes or No

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  • #30546

    Pomara
    Member

    Can someone tell me if there is a statiscal difference if I have aP value of 0.150

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    #79637

    Picklyk
    Participant

    short answer:Β  no.
    Β 

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    #79639

    Ralph
    Participant

    From the dictionary: The p-value is compared with the desired significance level of our test and, if it is smaller, the result is significant. That is, if the null hypothesis were to be rejected at the 5% significance level, this would be reported as “p < 0.05".
    So if you’re running a 5% significance test, the p value would need to be less than 0.05 for it to be significant.

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    #79640

    Scott
    Member

    A p value of < 0.05 is usually required to denote a significant difference.
    Rick

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    #79641

    Robert Butler
    Participant

    A P value is just the value of alpha at which a decision regarding the null hypothesis would just be on the borderline between acceptance and rejection.Β  It is a common practice to set alpha = .05 or .01, however, there is nothing sacred about either of these numbers.Β  In many fields an alpha level of .05 is unreasonable.Β 
    Β  A P value of .05 means that you can reject the null hypothesis at a level of significance of .05 and a P value of .15 means that you can reject the null hypothesis at a level of significance of .15.Β  In other words, at .05 there is a 5% chance that the observed difference isn’t statistically significant and at .15 there is a 15% chance that the difference isn’t statistically significant.
    Β So, to answer your questioin, yes there is a statistical difference at a P of .15. The question that you need to address is-are you willing to live with the possibility that there is roughly a one-in-seven (actually 6.6667)Β  chance that you are wrong as opposed to a one-in-20 chance of being wrong if you go with a P=.05

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    #79664

    Mike Carnell
    Participant

    Robert,
    Nice answer. I am glad someone finally got it right.
    The other part of the answer (not necessarily for Robert)Β is that you select alpha, beta and the amount of shift before you run the test so you don’t have to ask these questions when you already have a result.
    You can select any alpha and beta level you want and drive it into significance by inflating the sample size (which is what most people do). If you select a large enough sample size you can get significance from a t test withΒ aΒ 0.1 sigma shift.
    There is a table which is generally available (if you didn’t get it during BB training you should as why not) that will simplify this. If youΒ can’t find it email me at [email protected] and I will get you a copy.
    Good luck.Β 

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    #79669

    Robert Butler
    Participant

    In his post Mike Carnell re-emphasizes a key point of all statistical analysis-namely that one should always choose the statistical methods of inquiry before running the analysis-not after.Β  If you don’t do this then, as he points out, it is a very easy matter to cast around and find some statistical tool that will support whatever position it is that you wish to defend.
    Β  The well known statistician, Stuart Hunter, (of Box, Hunter, and Hunter fame) refers to this kind of abuse of statistics as P.A.R.C. analysisΒ  – Planning After Research Completed….and he points out that what you have when you analyze your data in this manner is exactly what you get when you spell P.A.R.C. backwards.

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    #79671

    Mike Carnell
    Participant

    Robert,
    Thanks. I hadn’t seen that in years and couldn’t remember what it stood for.
    Just as an example from the original post. You have a P value of 0.15. Choose a confidence level of 80%. Alpha becomes 0.2 and you have instant statistical difference.
    Thanks again.

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