A brief recap of the Brute Force Method
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.|
Copyright University of Maryland, 2007
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