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)
“Today so much rebellion is aimless and demoralizing precisely because children have no values to challenge. Teenage rebellion is a testing process in which young people try out various values in order to make them their own. But during those years of trial, error, embarrassment, a child needs family standards to fall back on, reliable habits of thought and feeling that provide security and protection.”
—Neil Kurshan (20th century)