From fbcb457bb11371e034dd8fa5e9f7dc34b807ee96 Mon Sep 17 00:00:00 2001 From: zleyyij Date: Wed, 17 Jan 2024 14:25:19 -0700 Subject: [PATCH] vault backup: 2024-01-17 14:25:19 --- education/statistics/Central Limit Theorem.md | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) 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. \ No newline at end of file +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. \ No newline at end of file