#TYPE II ERROR PLUS#
Shepherd thinks wolf is NOT present (shepherd does nothing) when a wolf is actually presentĬosts (actual costs plus shepherd credibility) associated with scrambling the townsfolk to kill the non-existing wolf Shepherd thinks wolf is present (shepherd cries wolf) when no wolf is actually present Let’s start with our shepherd / wolf example. Let’s walk through a few examples and use a simple form to help us to understand the potential cost ramifications of type I and type II errors. This is a type II error or false negative error.Ī tabular relationship between truthfulness/falseness of the null hypothesis and outcomes of the test can be seen in the table below: That is, the actual situationwas that there was a wolf present however, the shepherd wrongly indicated there was no wolf present and continued to play Candy Crush on his iPhone. Again, our null hypothesis is that there is “no wolf present.” A type II error (or false negative) would be doing nothing (not “crying wolf”) when there is actually a wolf present. A Type II error is committed when we fail to believe a true condition.Ĭontinuing our shepherd and wolf example. Let me say this again, a type II error occurs when the null hypothesis is actually false, but was accepted as trueby the testing.Ī type II error, or false negative, is where a test result indicates that a condition failed, while it actually was successful. Type II Error (False Negative)Ī type II error occurs when the null hypothesis is false, but erroneously fails to be rejected. Let’s say that our null hypothesis is that there is “no wolf present.” A type I error (or false positive) would be “crying wolf” when there is no wolf present. That is, the actual conditionwas that there was no wolf present however, the shepherd wrongly indicated there was a wolf present by calling “Wolf! Wolf!” This is a type I error or false positive error. This false positive error is basically a “false alarm” – a result that indicates a given condition has been fulfilled when it actually has not been fulfilled (i.e., erroneously a positive result has been assumed).
Let me say this again, a type I error occurs when the null hypothesis is actually true, but was rejected as falseby the testing.Ī type I error, or false positive, is asserting something as true when it is actually false. Type I Error (False Positive Error)Ī type I error occurs when the null hypothesisis true, but is rejected. However, if the result of the test does not correspond with reality, then two types of error are distinguished: type I errorand type II error. If the result of the test corresponds with reality, then a correct decision has been made (e.g., person is healthy and is tested as healthy, or the person is not healthy and is tested as not healthy). The result of the test of the null hypothesis may be positive(healthy, not guilty, not broken) or may be negative(not healthy, guilty, broken). The statistical test requires an unambiguous statement of a null hypothesis (H 0), for example, “this person is healthy”, “this accused person is not guilty” or “this product is not broken”. In statistical test theory, the notion of statistical error is an integral part of hypothesis testing. Let me use this blog to clarify the difference as well as discuss the potential cost ramifications of type I and type II errors. I have also provided some examples at the end of the blog. I recently got an inquiry that asked me to clarify the difference between type I and type II errors when doing statistical testing. Reviving from the dead an old but popular blog on Understanding Type I and Type II Errors