Hypothesis testing and p-values | Inferential statistics | Probability and Statistics | Khan Academy
Why Cross Validated Stack Exchange recommends it
Cross Validated users link it as a basic worked example of a one-sample t-test and as a quick refresher on what a hypothesis test and p-value mean.
Recommended by 1 people across 1 different months, 2014 to 2015.
What it is
An 11-minute Khan Academy lesson by Sal Khan introducing hypothesis testing and p-values in inferential statistics. It works through a worked example of a drug's effect on rat response times, framing the null hypothesis and computing how likely the observed sample mean is under it.
What people said
Recommends rb612 · 2015-10 · Cross Validated Stack Exchange · on “Why can we use sample variance of the treatment group to estimate population variance of the controls?”
Let's say you have the following basic one-sample t-test, taken from Khan Academy [this video] : A neurologist is testing the effect of a drug on response time by injecting 100 rats with a unit dose of the drug, subjecting each to neurological stimulus, and recording its response time. The neurologist knows that the mean response time for rats not injected with the drug is 1.2 seconds. The mean of the 100 injected rats' response times is 1.05 seconds with a sample standard deviation of 0.5 seconds. Do you think that the drug has an effect on response time? When reviewing inferential stats, I was rethinking an issue I didn't understand with t-testing. What if the sample standard deviation for rats
Cites Arun Jose · 2014-04 · Cross Validated Stack Exchange · on “Self Study on Simple Linear Regression (Not So Simple Afterall)”
This is to answer your question (b). I'm assuming you know what a hypothesis test [this video] is, and this reply will use it to demonstrate how the answer is arrived at. A p-value that is computed for daily wages has a null hypothesis that the slope of that parameter is equal to 0. What this implies is that daily wages has no relationship to quit rate. (I'm simplifying here only for simple linear regression and not multiple regression.) If you find your p-value to be less than 0.05, it means that this hypothesis has a very small chance of being true or accepted. So we would conclude that the null is to be rejected and daily wages has a non-zero slope, hence a relationship to quit rate. In the an
Cites Charlie Parker · 2014-12 · Cross Validated Stack Exchange · on “What does Bayesian Hypothesis Testing mean in the framework of inference and decision theory?”
My background is mainly in machine learning and I was trying to learn what Bayesian Hypothesis testing meant. I am ok with the bayesian interpretation of probability and I am familiar with it in the context of probabilistic graphical models. However, what is confusing me is what the word "Hypothesis" means in the context of statistical inference. I think I am mostly getting confused about the vocabulary that I am used to in machine learning vs what is normally used in statistics and inference. In the context of supervised learning [LINK] , I normally think of the hypothesis as the predictive function that maps examples to its labels i.e. $h:\mathcal{X} \rightarrow \mathcal{Y}$. However, it s