# 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 raw deviation before dividing by the expected value. So we have the following sequence:

### 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?

(To make this problem interactive, turn on javascript!)

- I need a hint ... : A big chi-square probably means that the individual

numbers you added were also big...

- ...another hint ... : The individual numbers you added were deviations from

the model predictions.

#### 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.

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