Hello everyone,<\/p>\n\n\n\n
Type II error is an error that occurs when you accept the null hypothesis when in the real sense, it is false. It accepts the results of a test as negative when they are positive or false (False negative). Peter Donnelly explains this type of error with a scenario of HIV test results. A test for HIV is 99% accurate. Let\u2019s say in a group of a million, 900,000 people test negative. Because the test is 99% accurate, out of the 900,000 people, there is the probability that close to 9,000 people are positive but tested negative.<\/p>\n\n\n\n
In the scenario above, most people only think about the false positive. We hope that just in case a medical test (HIV, Cancer, or any other terminal illness) is positive, we are lucky enough that it is a false positive. Donnelly, in his video, explains how hard it is to get a false positive compared to a false negative because the chances of getting the disease by itself are low. None of the errors are better than the other; I believe it depends on the statistical decision being made. For example, in the scenario above, although we seem to be more concerned about a false positive, a type II error is worse. It means you have the disease, and you might end up finding out when it is too late.<\/p>\n\n\n\n
In the case of the lousy statistician, the lawyers or the jury would have noticed the argument was wrong if they had the right decision-making skills. Therefore, I still think statistical thinking is vital for the general population, and as I said in my previous discussion, it is something everyone should learn to do correctly. Because it helps reach the correct conclusions and identify when statistical errors are being made.<\/p>\n","protected":false},"excerpt":{"rendered":"
Hello everyone, Type II error is an error that occurs when you accept the null hypothesis when in the real sense, it is false. It accepts the results of a test as negative when they are positive or false (False negative). Peter Donnelly explains this type of error with a scenario of HIV test results. […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[5],"tags":[],"class_list":["post-782","post","type-post","status-publish","format-standard","hentry","category-psychology"],"yoast_head":"\n