From ecb188ce260d9933e82fad1e67e33b852be98cc0 Mon Sep 17 00:00:00 2001 From: zleyyij Date: Tue, 13 Feb 2024 13:57:56 -0700 Subject: [PATCH] vault backup: 2024-02-13 13:57:56 --- education/statistics/Hypothesis Tests.md | 13 ++++++++++++- 1 file changed, 12 insertions(+), 1 deletion(-) diff --git a/education/statistics/Hypothesis Tests.md b/education/statistics/Hypothesis Tests.md index 81ce7e2..1a33502 100644 --- a/education/statistics/Hypothesis Tests.md +++ b/education/statistics/Hypothesis Tests.md @@ -49,10 +49,21 @@ 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. +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, evidence. If the **p-value is greater than *5*%, fail to reject** the null hypothesis, not enough evidence. +Investigators should: +- Summarize the data +- Say what test was used +- Report the p-value + +Investigators should not: +- Blindly compare P to 5% or 1%, there are real world factors that change how important this value is. + +### Data Mining/Snooping + + (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).