Statistics / Unit 9: Hypothesis Testing Basics
9.2 P-Values
Notes
A p-value is the probability of getting a result at least as extreme as the one observed, assuming $H_0$ is true.
- A small p-value means the data would be unlikely if $H_0$ were true, which is evidence against $H_0$.
- A large p-value means the data are consistent with $H_0$.
- The p-value is NOT the probability that $H_0$ is true.
- Example: $p\text{-value} = 0.03$ means that if $H_0$ were true, there would be only a $3\%$ chance of results this extreme or more.
Practice
Which p-value gives the strongest evidence against $H_0$?
The smaller the p-value, the stronger the evidence against $H_0$.
A test gives a p-value of $0.03$. Which is the correct interpretation?
A p-value is a probability computed assuming $H_0$ is true.
A p-value is defined as the probability of:
It measures how surprising the data are under $H_0$.
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