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Thread: Assumptions of statistic models

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    Assumptions of statistic models




    I was watching [Hadley's dplyr video](https://www.youtube.com/watch?v=8SGif63VW6E) and he said a statement that made me think:

    Quote Originally Posted by Hadley Wickham
    statistical models ``don’t fundamentally surprise you''


    I asked for clarification via Twitter and he said:

    Quote Originally Posted by Hadley Wickham
    models make assumptions that by their nature, can't question...also note that it's never possible to test assumptions; you either assume them or not
    I'm curious what others think about these comments. Do people agree?

    Is he saying the model is a coat that either fits the data or not; whereas visualization is asking the data what type of coat will fit?
    "If you torture the data long enough it will eventually confess."
    -Ronald Harry Coase -

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    Re: Assumptions of statistic models


    Here's how I interpret it. Take the example of a simple regression model that assumes normal errors. There is nothing in the model itself that tells us whether that assumption is reasonable for a given set of data--nothing in the parameter estimates or predicted values, for example. Of course, we can try to verify this assumption through other means, such as a QQ plot or density plot of the errors. But these are things that are outside of the regression model itself.
    “In God we trust. All others must bring data.”
    ~W. Edwards Deming

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