Test for statistical significance in performance variability I'm analysing data collected from a call centre activity in which agents are calling prospective leads. For each call made, if the lead coverts the call is a success and if the lead does not convert the call is a fail. 
The data are for 5 agents. Each agent made a different number of calls in a unspecified period of time, and each has a different success rate. Boiled down, I have 5 percentages for the success rates. 
I need to find a way to determine if the variability of the success rates is statistically significant, but can't think of an appropriate test. The main problem being that I have no benchmark value to test against. Of course, the data should have been collected in a better-designed experiment. However this is a typical 'here's some data' in an Excel spreadsheet scenario. 
None of the tests I'm more familiar such as t-tests, Binomial tests, ANOVA, CHI Sq GOF, regressions, hypothesis testing against a null value, etc, seem to make sense. Is there a method I'm not thinking of for testing statistical significance in the variability in performance with this type of data?
 A: Why not simply a Chi-Squared test of independence over this kind of table ?
                 +-----+-----+-----+-----+-----+
                 | Ag_1| Ag_2| Ag_3| Ag_4| Ag_5|      
+----------------+-----+-----+-----+-----+-----|
| SuccessCount   |  20 |  5  |  10 |  40 |  21 | 
| FailCount      |  30 |  30 |  10 |  30 |  11 |
+----------------+-----+-----+-----+-----+-----+

A: Ok, thanks - though I'm not sure I follow the theory of how a Chi-Squared Independence test applies to the data. I've used the 'answer' field to respond as this might be the correct solution. Let me know what you think. 
Let's say I set hypotheses as follows:


*

*Null: variation in signups-per-call by agent is a result of chance.

*Alternative: variation in signups-per-call by agent is statistically significant and not a result of chance. 


And in Python (SciPy) construct the test: 
ag_1 = [326, 1908]
ag_2 = [263, 1478]
ag_3 = [81, 750]
ag_4 = [53, 339]
ag_5 = [45, 199]

data = [ag_1, ag_2, ag_3, ag_4, ag_5]
stats.chi2_contingency(data)

Results:
X-squared = 21.407
Degrees of freedom = 4
p-value = 0.0002629

So I reject the null: the variability in signups-per-call by agent is statistically significant. At least in theory anyway. In reality, as this is an observational study, I think there's a whole host of confounders that muddy the inference. 
