# Should I use chi-squared?

I have a dataset reporting a number of incidents for a given population, such as:

Country Nb incidents    Population  Rate
A             1               30    3.33%
B            71            15000    0.47%
C             4             2000    0.20%
D             1              600    0.17%
E            19            12000    0.16%
F             3                3     100%


My boss asked me to ignore countries having only one incident. Country A is incorrectly flagged as a problem, so we should not take it into account at all and concentrate on country B instead.

But I'd like to implement a more robust and efficient solution. My problem is that country A has the highest Rate of incidents, but it is obviously not significant since the tested population is very small. Country D has also only one incident but the tested population is large enough.

As an extreme example, I have added a country F where the rate is 5000%. Here, we have to consider the result because a lot of incidents for a small population would mean we have a huge problem ;)

I thought chi-squared would be OK, but I have no idea how I could use it on this data set.

• What does it mean to "rip" a country? What are you ultimately trying to do (ie, what will you do after ripping)? Feb 28, 2018 at 15:58
• I have edited my post, what I meant was that we should ignore this entry completely. Ultimately, we want to priorize actions : the country with the highest rate should be considered first to prevent further incidents from happening
– Mike
Feb 28, 2018 at 16:05
• So the idea here is your firm wants to focus its efforts on the country w/ the biggest problems, as measured by the observed rate of incidents, is that right? Are you just trying to identify the worst, or do you need to rank all of them, or do you want the best estimate of their true rates? Feb 28, 2018 at 16:18
• The biggest absolute problem is wherever there are most incidents. The biggest relative problem is wherever the rate is highest. If data are known independently to be wrong, ignore them. Sorry, but I am not clear what else is there is to say. (Evidently your data are just fake; we understand, but there is no analysis to do there.) Feb 28, 2018 at 16:22
• To start with, it would be OK to correctly identify the worst without wrongly flagging country A as the worst (since it is an obvious outlier). I am looking for a more general method than just "deciding" that 1 incident for 30 population is not OK (why not 2 for 30 e.g.). So, yes, the idea is to focus on the country with the biggest problems as measured by the rate of incidents
– Mike
Feb 28, 2018 at 16:23