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Yield comparison

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  • #32935

    Cordell Fife
    Participant

    Hello,
    I would like to compare two sets of yield data. The yield data
    are written in percentage (80% yield means that 100% went in
    to a production line and 80% came out).
    There are more than 100 yield data for each set.
    Is it OK to use t-test for this comparison to see which set has
    high yield?
    Since this yield = (1-defect percentage), I was wondering if I
    need to use something else, such as poisson comprison.
    Thank you,
    Kim

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    #88502

    howe
    Participant

    Hi Kim,
    It’s an interesting question, the yield data are plotted as negative skew on Normal Probability chart since most of your data fall in the high end between the range from 0% to 100%, so it is not nomal distribution data set. Personally I don’t know if it is correct or not by using t-test (2 samples) to assess the difference, but I think you still can use it, my concern is the “mean” here donesn’t mean anything ( what does 85% average from a data set mean?).
    I think another method could be used, Box Plot chart, to see the difference. This is more directly and visible.
    I would like also to share any ideas on this questions.
    Thanks,
    Mike

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    #88503

    Sigma Singh
    Member

    Kim
    By design t test is meant to test the equality of means of “continuous” data. Now – yield is inherently derived from attribute data. how many passing or failing data points (not mentioning how well they passed). Hence chi square test is the right comparision in this scenario.
    sigma singh

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    #88507

    Bee
    Participant

    Kim,
    Since yield is measured in % (and therefore continuous), I would vote for t test.
    However testing variances (SD) is also very important before applying t test. You could use Box plots for this.
    Bee

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    #88520

    Larry
    Participant

    Strictly speaking, yield is in fact a discrete metric.  However, many projects that I see treat it as a continuous metric to allow the use of better analysis tools.  Statistically, this may not be the greatest way to proceed, but it’ll get you to improvement, so I say go ahead.  However, if the data is not normal, why bother testing the means?  I must agree that Chi Squared is probably the way to go.  Or a proportion test.

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    #88559

    marklamfu
    Participant

    Yield is typical attribute data , t-test is used to comparing 2 sets variable data, if you want to use T-test or Poisson for attribute data, I believe, it is not accurate, Generally, we can plot unit(daily, weekly) yield as a trend for comparing, or transfer attribute as a variable data and then use t-test or other tool(s) for comparision,

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