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Sample Size Justification

Discussion in 'Sampling, Standards and Inspection' started by TheWaeller, Mar 23, 2016.

  1. TheWaeller

    TheWaeller Member

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    Hi all,

    I'm looking for a guidance to justify sample size.

    Actually , the assumption is that the data sets (metric data) are following a student-t-distribution. Then based on a T/s
    (Tolerance range above standard deviation)-ratio the sample size is calculated.

    This approach is being used for design verification. Usually max.sample size is 60 in accordance with ISO 11608-1.

    What about if the assumption will not be true? Do Ineed to go to a distribution free approch?

    Thanks for any hint
    Waeller
     
  2. Vthouta

    Vthouta Member

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    You can justify using the level of significance based on your current problem definition. This would lead to type-1 and type 2 errors. I just got to know effect size could be used to justify, but I have not applied yet to any of my problem definitions. I am not sure whether I answered your question.

    Link:https://en.wikipedia.org/wiki/Effect_size
     
  3. Bev D

    Bev D Moderator Staff Member

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    Assumptions are tough. the t-distribution is fairly robust tho. you could perform a goodness of fit test. But this is all about justifying a sample size based on statistical theory to who?
    if it's to your organization, I might suggest a better way depending on the risk (severity of effect) of the product you are validating. If it's to a regulatory agency, you are probably OK as is. (depending on your reviewer)

    I would also recommend that you read the following article on tolerance intervals by Donald Wheeler.
     
    TheWaeller likes this.
  4. TheWaeller

    TheWaeller Member

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    Hi Bev,

    thank you for your help.
    The article of Don Wheeler is of of great help.
    Unfortunately, it is not yet available in his reading room.

    Thanks
    Waeller
     

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