Hypothesis Testing
In traditional statistical hypothesis testing, the tester starts with a null hypothesis and an alternative hypothesis, performs an experiment, and then decides whether to reject the null hypothesis in favour of the alternative. Hypothesis testing is therefore a binary classification of the hypothesis under study.
A positive or statistically significant result is one which rejects the null hypothesis. Doing this when the null hypothesis is in fact true - a false positive - is a type I error; doing this when the null hypothesis is false results in a true positive. A negative or not statistically significant result is one which does not reject the null hypothesis. Doing this when the null hypothesis is in fact false - a false negative - is a type II error; doing this when the null hypothesis is true results in a true negative.
Read more about this topic: Binary Classification
Famous quotes containing the words hypothesis and/or testing:
“Oversimplified, Mercier’s Hypothesis would run like this: “Wit is always absurd and true, humor absurd and untrue.””
—Vivian Mercier (b. 1919)
“Is this testing whether I’m a replicant or a lesbian, Mr. Deckard?”
—David Webb Peoples, U.S. screenwriter, and Ridley Scott. Rachel, Blade Runner, being tested to determine if she is human or machine (1982)