I'm doing a Chi Square analysis to look at whether two variables are dependent or independent when it comes to the pass rate on a test with two variables: the origin of a participant and the year they take the test. The result comes out as non-significant (.07, so maybe a trend toward significance.)
| Year | PlaceX | PlaceY |
| 2015 | 30 | 10 |
| 2016 | 30 | 2 |
However, when I look at the fail rate, shown below, the p value is much 'less' significant:
| Year | PlaceX | PlaceY | |------|--------|--------| | 2015 | 270 | 30 | | 2016 | 270 | 38 |
I don't understand why I don't get the exact same significance value given that the two sets of numbers are mirror images of one another - one is the pass rate, one is the fail rate, and the total pass / fail rate for each Place add up to the same total sample size for the two years.
Any light shed on this would be great - I feel like I'm being dishonest by looking at the pass rate as this shows a specific result that trends toward significance, when I could just as easily look at the fail rate and have this be highly non-sig.