Fisher’s Exact Test

Mala Mahadevan explains Fisher’s Exact Test and provides examples in T-SQL and R:

The decision rule in two sample tests of hypothesis depends on three factors :
1 Whether the test is upper, lower or two tailed (meaning the comparison is greater, lesser or both sides of gender and speaker count)
2 The level of significance or degree of accuracy needed,
3 The form of test statistic.
Our test here is to just find out if gender and speaker count are related so it is a two tailed test. The level of significance we can use is the most commonly used 95% which is also the default in R for Fischer’s Test. The form of the test statistic is P value. So our decision rule would be that gender and speaker category are related if P value is less than 0.05.

Click through for the R code followed by a code sample which should explain why you don’t want to do it in T-SQL.