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| author | sotech117 <26747948+sotech117@users.noreply.github.com> | 2024-03-11 15:42:17 -0400 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2024-03-11 15:42:17 -0400 |
| commit | 0b4b0eea02dcb9d89bdc7338a71a7e91817aa62b (patch) | |
| tree | 0982ebb3862bad876ba7dd9ee5b457ab513a6e33 | |
| parent | 6cbc309cb94551e75d914e105ff94e5a39878abf (diff) | |
Update README.md
| -rw-r--r-- | README.md | 4 |
1 files changed, 2 insertions, 2 deletions
@@ -16,9 +16,9 @@ While the velocity for each dimension in our system (x, y, z) has a normal distr 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. 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 directions at energy level this low and I found it (in your sampleA vs sampleB calculations), then it's highly unlinkely this state is a coicidence (violates the second law of thermodynamics) and the null hypothesis can be rejected (i.e. these distributions are not the same)." +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)." In theory, for performing a hypothesis test with categorical variables, we take each dimension to be the difference between the normal distrubtions (of differences in observed-expected) of the samples. This encapsulates the difference between the distrubtions into a normal curve, which we combine into the chi^2 curve (visuals helps this explanation, see video). ## Video of Special Topic Hour -TODO: add video after the hours
\ No newline at end of file +TODO: add video after the hours |