In statistics, a false positive is also known as a type I error and a false negative is also called a type II error. When designing an experiment, scientists get to decide on the probability of each type of error they are willing to tolerate. The most common false positive rate chosen is 5 percent. (This rate is also denoted by the Greek letter α, alpha, which is equal to 100 minus the confidence level. This is why you typically see people say a confidence level of 95 percent.) That means that, on average, if your hypothesis is false, one in twenty experiments (5 percent) will get a false
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