# Choosing a significance test for employee performance

I'm analyzing employee performance at a call-center. I'd like indicate whether a particular employee's conversion (success) rate is significantly different from the average/expected conversion rate. Some factors for consideration:

• there is a wide range of events for each employee. i.e. some employees have only taken 200 calls, while others have taken over 2,000 calls.
• there are 2 types of conversions. the first type has an average conversion rate of around 15%, while the other is around 80%. That may be irrelevant because I can just separate them into two separate tests.

Initially, I was using a chisquare test from scipy.stats, but now I'm wondering if I should use the chi2_contingency test or something else. I'm worried that the chisquare test does not take into account the number of calls an operator takes, only the difference between the observed and expected amount of conversions (i.e. expected = (average conversion rate)*(total operator calls)). Would it be wrong to do a normal t-test to test each operator against the whole population?

Additionally, would conversions count as poisson distribution and does that make a difference to what test to use?

• What do you mean by conversion rate ? What is some employees ? – Subhash C. Davar Oct 25 '17 at 4:29