GraphPad Statistics Guide

Type I, II (and III) errors

Type I, II (and III) errors

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Type I, II (and III) errors

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Type I and Type II errors

When you make a conclusion about whether an effect is statistically significant, you can be wrong in two ways:

You've made a type I error when there really is no difference (association, correlation..) overall, but random sampling caused your data to show a statistically significant difference (association, correlation...). Your conclusion that the two groups are really different (associated, correlated) is incorrect.

You've made a type II error when there really is a difference (association, correlation) overall, but random sampling caused your data to not show a statistically significant difference. So your conclusion that the two groups are not really different is incorrect.

Type 0 and Type III errors

Additionally, there are two more kinds of errors you can define:

You've made a type 0 error when you get the right answer, but asked the wrong question! This is sometimes called a type III error, although that term is usually defined differently (see below).

You've made a type III error when you correctly conclude that the two groups are statistically different, but are wrong about the direction of the difference. Say that a treatment really increases some variable, but you don't know this. When you run an experiment to find out, random sampling happens to produce very high values for the control subjects but low values for the treated subjects. This means that the mean of the treated subjects is lower (on average) in the treated group, and enough lower that the difference is statistically significant. You'll correctly reject the null hypothesis of no difference and correctly conclude that the treatment significantly altered the outcome. But you conclude that the treatment lowered the value on average, when in fact the treatment (on average, but not in your subjects) increases the value. Type III errors are very rare, as they only happen when random chance leads you to collect low values from the group that is really higher, and high values from the group that is really lower.