# A brief recap of the Brute Force Method

## General Steps |
## In the Dilbert example... |

1. Decide on a null hypothesis -- a "model" that the data should fit |
Dilbert's null hypothesis was that the sickdays were randomly distributed. |

2. Note your "expected" and "observed" values |
Since 40% of weekdays fall on Monday or Friday, the same should be true of sickdays -- or 40 out of 100. The observed value was 42 out of 100. |

3. Simulate lots of data |
We simulated 100 trials with the applet. |

4. Decide what your "threshold of pain" is (otherwise known as a p-value).
*Note: technically this should come before simulating your data! |
We picked a threshold of 5%, or 5 out of 100 trials |

5. Determine whether the agreement of the simulated data with the observed
data falls within the threshhold -- if so, we say the model fits the data well. |
Since the simulated data showed many more than 5% of trials with at least 42 mon/fri sickdays, we decide that the model (random sickdays) fits the data. |

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