Detecting Outliers Of A Feature Relating to A Second I have a measurement, let's say number of forest fires in each state, and populations of those states. How can I create a statistical test that would detect outlier fires in each state that are disproportionate to the population of that state? For example if Montana has 1/1000th of US population but has 1/10th of fires, this measure should detect that. What approaches can be used here?

One way to compute this could be utilizing proportion z-tests. This test compares two independent population proportions, I could, per state basis, calculate state pop vs total pop and fires in that states / all fires and run the z-test on those proportions, then check for significance.
In Python statsmodels there is a simple library call to compute this,
import numpy as np
from statsmodels.stats.proportion import proportions_ztest
count = np.array([5, 12])
nobs = np.array([83, 99])
stat, pval = proportions_ztest(count, nobs)
print('{0:0.3f}'.format(pval))

In the example, the pvalue from the call is >0.05 which means H0, two
proportions being same, is not rejected.
