Statistics How To

Beta Level: Definition & Examples

Statistics Definitions > Beta Level

You may want to read this article first: What is an Alpha Level?

What is a Beta Level?

beta level

A Type I error is the incorrect rejection of a true null hypothesis. A Type II error is where you don’t reject a false null hypothesis.

Alpha levels and beta levels are related: An alpha level is the probability of a type I error, or rejecting the null hypothesis when it is true. A beta level, usually just called beta(β), is the opposite; the probability of of accepting the null hypothesis when it’s false. You can also think of beta as the incorrect conclusion that there is no statistical significance (if there was, you would have rejected the null).

Beta and Power

Beta is directly related to the power of a test. Power relates to how likely a test is to distinguish an actual effect from one you could expect to happen by chance alone. Beta plus the power of a test is always equal to 1. Usually, researchers will refer to the power of a test (e.g. a power of .8), leaving the beta level (.2 in this case) as implied.

How do I Lower Beta?

In theory, the lower beta, the better. You could simply increase the power of a test to lower the beta level. However, there’s an important trade-off. Alpha and beta levels are connected: you can’t lower one without raising the level of the other. For example, a Bonferroni correction reduces the alpha level (i.e. the probability of making a type II error) but inflates the beta level (the probability of making a type II error). False positives are minimized, but with the payoff that the possibility of false negatives are increased.

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