Here’s a summary of the steps needed to do a chi-square goodness of fit test:
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. Calculate the chi-square [add up (o-e)2 / e ] | We got 0.166 |
| 4. Look up the chi-square-crit based on your p-value and degrees of freedom. | With p=0.05 and df=1, chi-square-crit = 3.84. |
| 5. Determine whether chi-square-calc < chi-square crit– if so, we say the model fits the data well. | Chi-square-crit > chi-square-calc, so the deviations are small and the data fit the null model of random sickdays. |