5: The chi-squared equation

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 (that is, divide).

Add it all up. (This gives a value that we will call a chi-squared statistic. Why “chi-squared” I hear you ask – this is another one of those instances when a degree in Statistics helps!).

Place mouse on picture for more explanation

If the final chi-squared statistic is a big number, would this make you think that the data fits the model, or does not fit the model?

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