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MSA paired test

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  • December 9, 2009 at 9:00 am #53016

    Angler
    ★ 10 Years ★
    Participant

    Hi All,I must be wrong we doing the following statement, but I can’t see where…If someone could help me…I’ve done a MSA on a testing machine. This machine has 2 parallel equipments, which should give the same results. I considered them as 2 different operators. MSA result is very good (%Reproductibility = 6%, % Repeatability = 2%).
    Then, I concluded that I can say Equipment 1 and Equipment 2 give the same acceptable result.But, if I do a paired t-test using the results of this MSA, I find p=0, than Equipment 1 seems to be different from Equipment 2.Where am I wrong??Thx for reading till the end!

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    December 9, 2009 at 9:15 am #187415

    Darth
    ★ 10 Years ★
    Participant

    You are mixing apples and oranges. Gage R&R tests the variability of your measurement system. Your hypothesis test examines whether the means of the two samples come from different populations. Try a two variance test and see what happens. There should be no assumption that the paired t test and Gage R&R will result in similar decisions regarding the equipment.

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    December 9, 2009 at 9:21 am #187416

    Angler
    ★ 10 Years ★
    Participant

    That’s clear, thank you Darth

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    December 9, 2009 at 10:29 am #187417

    Craig
    ★ 10 Years ★
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    sounds like you are evaluating repeatabilty/ reproducibility as well as bias between you measurement systems.
    -what do the % values represent below? (% tolerance, % study variation,?)
    – how did you select your range of samples?
    – what was your total sample size?
     
     

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    December 9, 2009 at 11:26 am #187418

    Angler
    ★ 10 Years ★
    Participant

    – It’s the % of study variation.
    – The samples have been choosen in order to cover the whole range of possible production.
    – 10 different samples, measured 6 times with each equipmentOther information I didn’t mention earlier:
    I find a perfect correlation between the results of my 2 equipments (R²>99.99%)

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    December 10, 2009 at 10:02 am #187451

    Craig
    ★ 10 Years ★
    Participant

    Interesting…
    Now you have a repeatable and reproducible measurement system comprised of two perfectly correlated measurement tools, and they are different!
    Just out of curiosity, what is the tolerance on the parts and what is the range span of parts in production that you chose for the study? What was the % RR as compared to the tolerance (P/T ratio)?
     
     

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    January 11, 2010 at 8:00 am #188203

    Thothathiri
    ★ 10 Years ★
    Member

    Angler,
    Sorry for responding late.For you ideal statistical test is paird-t test, test
    10 parts in equipment 1 and the same parts to be
    used in testing in the equipment 2.You can analyze the result using Excel, the command
    is ttest and mention 1 in the type of test. Result
    what you get is p value, if its

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    January 11, 2010 at 8:37 am #188204

    Mikel
    ★ 10 Years ★
    Member

    if its

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    January 11, 2010 at 9:35 am #188206

    THOTHATHRI
    ★ 10 Years ★
    Member

    its p value based on the alpha level of 5%p values decides statistical significance of test. P value 0.05 means fail to reject Ho.

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    January 11, 2010 at 10:45 am #188208

    Mikel
    ★ 10 Years ★
    Member

    Much better – maybe that answer can be understood.

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    January 11, 2010 at 6:20 pm #188222

    Darth
    ★ 10 Years ★
    Participant

    Stan, while the answer may be more understandable it is still not clear whether he is correct or not. The null for a test of means is that the two population means from which the samples are drawn are the same. If he believes the test is to determine whether the sample means of the two pieces of equipment are equal then he is wrong. Of course they are different since the means will likely be mathematically different. The selected alpha says you are willing to be wrong 5% of the time if you state that the samples came from different populations. The p value is the true risk or % of the time you will be wrong if you reject the null. If the % probability of being wrong is greater than your desired risk then you don’t reject (p is high). If it is less than your acceptable risk (p is low)then you can reject the null with comfort. In hypothesis testing you are testing the difference in parameter means not the sample means. But then, maybe that is what he said after all.

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    January 12, 2010 at 5:10 am #188232

    Severino
    ★ 10 Years ★
    Participant

    I like how he snuck an ad-hoc sample size in there as well…

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    January 12, 2010 at 5:48 am #188234

    Darth
    ★ 10 Years ★
    Participant

    Yeah, wonder what the power is for a sample size of 10.

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