MathBench > Statistical Tests

Goodness of Fit Tests

One small correction

The method I showed you on the last page was not quite right. For reasons that are difficult to explain without a degree in statistics, you need to SQUARE the deviation before dividing by the expected value. So we have the following sequence:

    Determine what you "expected" to see.
    Find out the difference between the observed and expected values (subtract)
    Square those differences
    Find out how big those squared differences are compared to what you expected (divide)
    Add it all up.
arrow chi-square = sigma ( (o-e)^2) / e )
Place mouse on picture for more explanation

If the final chi-square is a big number, would this make you think that the data fit the model, or don't fit the model?


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I think I have the answer: Since the individual numbers you added
were deviations from the model predictions, a big chi-square means
the data deviate a lot. In other words, the model is a bad fit.