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. |
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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?
(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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