From 896fa1b74526a208d3fe549f840116f362a965fa Mon Sep 17 00:00:00 2001 From: zleyyij Date: Tue, 13 Feb 2024 13:52:56 -0700 Subject: [PATCH] vault backup: 2024-02-13 13:52:56 --- education/statistics/Hypothesis Tests.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/education/statistics/Hypothesis Tests.md b/education/statistics/Hypothesis Tests.md index 06c37c5..81ce7e2 100644 --- a/education/statistics/Hypothesis Tests.md +++ b/education/statistics/Hypothesis Tests.md @@ -50,11 +50,11 @@ Degrees of freedom ($df$) can be found by subtracting 1 from the sample size. Th The equivalent of $normalcdf$ for a t test is $tcdf$. This function returns a percentage. ## P Value The chance of observing at least a sample statistic, or something more extreme, if the null hypothesis is true. -If the p-value is less than *5*%, reject the null hypothesis. -If the p-value is greater than *5*%, fail to reject the null hypothesis. +If the **p-value is less than *5*%, reject the null** hypothesis, evidence. +If the **p-value is greater than *5*%, fail to reject** the null hypothesis, not enough evidence. -(Ch 28, stat 1040) -## Goodness of fit tests +(Ch 28-29, stat 1040) +## Goodness of fit tests ($\chi ^2$) This test is used when you have one qualitative variable with many categories, eg the (color, size, shape) of an (item). The $\chi^2$ curve does not follow the normal curve. It has a long right hand tail. As the degrees of freedom go up, the curves flatten out, and the hump moves out to the right.