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:
“The great tragedy of science—the slaying of a beautiful hypothesis by an ugly fact.”
—Thomas Henry Huxley (1825–95)
“Bourbon’s the only drink. You can take all that champagne stuff and pour it down the English Channel. Well, why wait 80 years before you can drink the stuff? Great vineyards, huge barrels aging forever, poor little old monks running around testing it, just so some woman in Tulsa, Oklahoma can say it tickles her nose.”
—John Michael Hayes (b.1919)