In the context of hypothesis tests, there are two sorts of errors: type I and type II. When a true null hypothesis is rejected (a "false positive"), a type I error occurs, and when a false null hypothesis is not rejected (a "false negative"), a type II error occurs.
The (unknown) disparity between the retained value and the true value is referred to as a statistical error. Because accuracy is defined as "the inverse of the whole error, including bias and variation," it is instantly related with accuracy (Kish, Survey Sampling, 1965). The lower the accuracy, the higher the error.
When you make a type III error, you correctly reject the null hypothesis, but for the wrong reason. This is in contrast to a Type I error (rejecting the null hypothesis wrongly) and a Type II error (rejecting the null hypothesis incorrectly) (not rejecting the null when you should).