# Computer = brute force

If you have clicked the button a few times, you've seen that the results vary quite a bit. Here are the first ten results that I got:

 Trial #: 1 2 3 4 5 6 7 8 9 10 Mondays/Fridays 43 33 37 47 40 37 39 41 41 51

What have we accomplished so far? First, we made a hypothesis about what causes sickdays -- namely, they happen on randomly chosen days. The competing hypothesis, held by the pointy-haired boss, is that sickdays occur disproportionately on Mondays and Fridays.

 First step: pick a hypothesis

Secondly, we calculated how many sickdays "should" fall on Mondays or Fridays according to our hypothesis -- that is, 40%, or 40 out of 100. This is the expected value of sickdays. But since we're dealing with a random process, we also expect some scatter around that expected value. The key question is, how much scatter?

 Second step: calculate the expected result

Next we used a simulation of randomly chosen weekdays to investigate how much scatter to expect around the 40 out of 100 prediction.

 Third step: simulate the scatter

### In the 10 trials, listed above, how common was it to get 42 or more Monday/Friday sickdays?

(To make this problem interactive, turn on javascript!)

• I need a hint ... : Try counting how many trials had at least 42 Mon/Fri sickdays

• ...another hint ... : 3 out of 10 trials had at least 42 Mon/Fri sickdays -- what percentage is that?

#### I think I have the answer: 3 out of 10 trials had at least 42 Mon/Fri sickdays, or 30%, which seems pretty common.

Time for a donut break!