From 4049af6f82e4d6d95d7a4aa86c174bef988abd80 Mon Sep 17 00:00:00 2001 From: zleyyij Date: Mon, 4 Mar 2024 12:41:03 -0700 Subject: [PATCH] vault backup: 2024-03-04 12:41:03 --- education/statistics/Central Limit Theorem.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/education/statistics/Central Limit Theorem.md b/education/statistics/Central Limit Theorem.md index 0e60b1f..fc58b6f 100644 --- a/education/statistics/Central Limit Theorem.md +++ b/education/statistics/Central Limit Theorem.md @@ -7,7 +7,7 @@ are sufficiently large. Probability histograms represent *chance*. Each class interval represents the probability an event would occur. As the number of repetitions increases, the closer the graphed data will appear to the calculated probability histogram. -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. +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.