DDE-1/Statistics.md

@@ -104,9 +104,9 @@ p(+|D) = (0.85)(0.01)/((0.85)(0.01)+(0.03)(0.99))
 p(+|D) = (0.0085)/(0.0382) = 0.22
 ```
 
-It seems even if the test seems to be relatively accurate to capture sick people, the probability of being measured as a sick person given that you are sick is 22 %. There is also a very nice [visual illustration here]( https://seeing-theory.brown.edu/bayesian-inference/index.html#section1) that you can play with.
+Even if the test seems to be relatively accurate to capture sick people, the probability of being measured as a sick person given that you are sick is 22 %. As the sickness gets more rare, the test results will be less and less reliable. There is also a very nice [visual illustration here]( https://seeing-theory.brown.edu/bayesian-inference/index.html#section1) that you can play with.
 
-How can we generalize the approach for N number of events?
+Our concern here is, how can we generalize the approach for N number of events?
 
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