diff --git a/education/statistics/Central Limit Theorem.md b/education/statistics/Central Limit Theorem.md
index c7df80c..0e60b1f 100644
--- a/education/statistics/Central Limit Theorem.md	
+++ b/education/statistics/Central Limit Theorem.md	
@@ -9,4 +9,8 @@ Probability histograms represent *chance*. Each class interval represents the pr
 
 The probability curve for the *sum of draws* will approximately follow the normal curve if the number of draws is large enough, even if the tickets in the box *do not *follow the normal curve. 
 
-When applying statistics to sums, it's usually in the form of *how much do we think the sum will add up to*, then compared against what it actually adds up to. The $EV_{sum}$ is used for for the estimated sum of all events. The $SE_{sum}$ refers to the standard error of the sum, or how much you expect the guess to be off by. This can be thought of like the standard deviation. 
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+When applying statistics to sums, it's usually in the form of *how much do we think the sum will add up to*, then compared against what it actually adds up to. The $EV_{sum}$ is used for for the estimated sum of all events. The $SE_{sum}$ refers to the standard error of the sum, or how much you expect the guess to be off by. This can be thought of like the standard deviation. 
+
+If the box is not uniform, the graph will not follow the normal curve as closely.
+
+The central limit theorem says that if a distribution is not normal, you can still follow the normal distribution if the number of draws is large, and the draws are random. 
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