Type I errors, also known as false positives, is the error of rejecting a null hypothesis when it is actually true. This can be viewed as a miss being registered as a hit. The acceptable rate of this type of error is called significance level and is usually set to be 0.05 (5%) or 0.01 (1%).

Type II errors, also known as false negatives, is the error of not rejecting a null hypothesis when the alternative hypothesis is the true. This can be viewed as a hit being registered as a miss.

Depending on the purpose of testing, testers decide which type of error to be concerned. But, usually type I error is more important than type II.