The null hypothesis is that the home remedy has no effect on headaches, while the alternative hypothesis is that it does heal headaches. Example of a type I errorĪ medical researcher is testing the efficacy of a home remedy for headaches. In the false negative, they incorrectly fail to reject the null hypothesis. In the false positive, the jury incorrectly rejects the null hypothesis that states the defendant is innocent. True positive: Found guilty in court and being guilty in factįor the above example, the second and third outcomes are type I and type II errors, respectively. True negative: Found innocent in court and being innocent in factįalse positive (type I error): Found guilty in court but being innocent in factįalse negative (type II error): Found innocent in court but being guilty in fact There are four possible outcomes with respect to the true nature of the case: Also known as a false negative, it occurs when a researcher does not reject the null hypothesis when the alternative hypothesis is true.įor example, in a court case, the null hypothesis would be that the defendant is innocent until proven guilty, and the alternative hypothesis would be that they're guilty. A type II error is the inverse of type I. A type I error, also called a false positive, is when a researcher rejects a null hypothesis that is true and decides there is a statistically significant difference that doesn't exist. Type I and type II errors both refer to incorrect determinations about a null hypothesis, but they differ in what the researcher decides to be true or false about the hypothesis. Related: Defining Hypothesis Testing (With Examples) Type I vs. In this article, we explain what type I and type II errors are, examine how they might occur, discuss their importance in research and provide examples to help you understand these concepts. Researchers test null hypotheses to see whether there is enough statistical significance to disprove it, and occasionally, this results in a type I or type II error. In hypothesis testing, a null hypothesis is a default hypothesis that states there's no statistical significance between variables.

Type 2 error