Type I error is an incorrect rejection of a true null hypothesis. The truth is that two variables in a research hypothesis (alternative hypothesis) are independent of each other, so there is no association between the two variables in reality. However, researchers often mistakenly conclude that those variables are related to one another. Simply put, Type I error can be understood as false positive. This entry provides an explanation of Type I error, offers an example, and discusses how to reduce Type I error rates.
Statistical testing is based on probability using data from a sample not from a population. Thus, although the selected sample well represents the population, errors might occur. That is, the decision researchers ...
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