# Review and Words of Wisdom

## Chi-square steps |

1. Decide on a null hypothesis -- a "model" that the data should fit |

2. Decide on your p-value (usually 0.05). |

3. Note your "expected" and "observed" values |

4. Calculate the chi-square [add up (o-e)^{2} / e ] |

5. Look up the chi-square-crit based on your p-value and degrees of freedom (df=rows-1). Determine whether chi-square-calc < chi-square crit. If so, we say the model fits the data well. |

The hardest steps are 1 (deciding on your null model) and 3 (figuring out what you "expected" to see based on the null model).

Usually your null model is that "chance alone" is responsible for any patterns in the observed data. For example, the 9:3:3:1 ratio for a dihybrid cross is what happens by chance alone, given that you mating 2 dihybrids.

This step (#1) also encompasses setting up your chi-square table or your simulations. For the chi-square table, you need to think in terms of **how many outcomes you have to test**. Each of these becomes a row. Now you also know the degrees of freedom for your test, which is the number of rows minus 1.

Step #3, finding the expected values, often means doing some probability calculations, using the Laws of AND and OR.

Once you know the expected values, filling out the rest of the chi-square table is just a matter of arithmetic.

*If you want a printer-friendly version of this module, you can find it here in a Microsoft Word document. This printer-friendly version should be used only to review, as it does not contain any of the interactive material, and only a skeletal version of problems solved in the module.*

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