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@@ -14,7 +14,7 @@ Notice how the distribution of random speeds and enegeries frequency graph is no
 
 While the velocity for each dimension in our system (x, y, z) has a normal distribution, the sum of the squares of them results in this non-symmetic, non-normal frequncy map, the chi distribution (not squared yet). Now chi^2 relates the energies of the system (speed^2), which is directly proportional the generalized chaos in the system (entropy). 
 
-Chi^2 is used to test the null hypothesis of "no difference" between categorical variables in AB testing because it measures generalized, non-directional chaos among all dimensions of the system - ideal for categorical variables. If your distrubtions from the dimensions are similar, it should converge to be highly-chaotic & high-energy, as stated by the second law of thermodynamics. By contrast, if your underlying distrubtions create an immensely low-chaotic (i.e. low-energy state), then it's highly likely these underlying distrubtions are different. 
+Chi^2 is used to test the null hypothesis of "no difference" among categorical variables in AB testing because it measures generalized, non-directional chaos among all dimensions of the system. If your distrubtions from the dimensions are similar, it should converge to be highly-chaotic & high-energy, as stated by the second law of thermodynamics. By contrast, if your underlying distrubtions create an immensely low-chaotic (i.e. low-energy state), then it's highly likely these underlying distrubtions are different. 
 
 Relating to AB testing, when you argue that, for chi^2, "if the p-value is less than 0.05, then the null hypothesis is rejected", you are saying that "if the probability of finding these distriubtions at an entropy this low and I found it (in your sampleA vs sampleB calculations), then it's highly unlinkely this state is a coincidence (violates the second law of thermodynamics) and the null hypothesis can be rejected (i.e. these distributions are not the same)."