![]() Note, these assume the original p value was 0.05. Doing so will give a new corrected p value of 0.01 (ie 0.05/5).īelow is a table containing some more examples of the number of multiple tests and the new Bonferroni-correct p values associated with them. ![]() To do this, I will divide the original p value ( 0.05) by the number of tests being performed ( 5). The first thing we need to do is to create a new Bonferroni-correct p value to take into account the multiple testing. Since all 5 memory tests are essentially measuring the same outcome, we will need to apply multiple comparison corrections to control for Type I error. We are now interested in determining if any of these memory test scores differ between the two age groups. Let’s say we have performed an experiment whereby a group of young and old adults were tested on 5 memory tests. The output from the equation is a Bonferroni-corrected p value which will be the new threshold that needs to be reached for a single test to be classed as significant. To perform the correction, simply divide the original alpha level (most like set to 0.05) by the number of tests being performed. The Bonferroni correction method is regarding as the simplest, yet most conservative, approach for controlling Type I error. Now you can understand the importance of controlling for multiple comparisons. In other words, in this situation, there is a 30% chance of discovering a false-positive result. Therefore, instead of a 5% rate, the Type I error rate is now 30%. For example, let’s say there is a hypothesis with 7 comparisons being performed – what is the probability of discovering a false-positive result? However, when there are multiple comparisons being made, the type I error rate will rise. This means that 5% of the time, you are willing to accept a false-positive result. ![]() Usually, the Type I error rate (the alpha level) in hypothesis testing is set to 5% (ie the p-value is 0.05). Type I error, in the context of hypothesis testing, is the likelihood of discovering a false-positive result, thus rejecting a true null hypothesis. Named after its Italian curator, Carlo Emilio Bonferroni, the Bonferroni correction method is used to compensate for Type I error. Simply, the Bonferroni correction, also known as the Bonferroni type adjustment, is one of the simplest methods use during multiple comparison testing. What is the Bonferroni correction method? In this guide, I will explain what the Bonferroni correction method is in hypothesis testing, why to use it and how to perform it. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |