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 wheels and springs of man are all set to the hypothesis of the permanence of nature. We are not built like a ship to be tossed, but like a house to stand.”
—Ralph Waldo Emerson (1803–1882)
“No testing has overtaken you that is not common to everyone. God is faithful, and he will not let you be tested beyond your strength, but with the testing he will also provide the way out so that you may be able to endure it.”
—Bible: New Testament, 1 Corinthians 10:13.