Skip to main content
Contents
Search Book
close
Search Results:
No results.
Dark Mode Prev Up Next \(\newcommand{\N}{\mathbb N}
\newcommand{\Z}{\mathbb Z}
\newcommand{\Q}{\mathbb Q}
\newcommand{\R}{\mathbb R}
\newcommand{\lt}{<}
\newcommand{\gt}{>}
\newcommand{\amp}{&}
\definecolor{fillinmathshade}{gray}{0.9}
\newcommand{\fillinmath}[1]{\mathchoice{\colorbox{fillinmathshade}{$\displaystyle \phantom{\,#1\,}$}}{\colorbox{fillinmathshade}{$\textstyle \phantom{\,#1\,}$}}{\colorbox{fillinmathshade}{$\scriptstyle \phantom{\,#1\,}$}}{\colorbox{fillinmathshade}{$\scriptscriptstyle\phantom{\,#1\,}$}}}
\)
Section 9.6 Glossary
hypothesis testing
A set of methods for checking whether an observed effect could plausibly be due to random sampling.
test statistic
A statistic used in a hypothesis test to quantify the size of an observed effect.
null hypothesis
A model of a system based on the assumption that an effect observed in a sample does not exist in the population.
permutation
A way to simulate a null hypothesis by randomly shuffling a dataset.
p-value
The probability of an effect as big as the observed effect, under a null hypothesis.
statistically significant
An effect is statistically significant if the p-value is smaller than a chosen threshold, often 5%. In a large dataset, an observed effect can be statistically significant even if it is too small to matter in practice.
