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Statistics vocabulary · Hypothesis Testing

type II error

Meaning in statistics, with examples

nounCommon term

In statistics, type II error means: Failing to reject a null hypothesis that is actually false, in other words missing an effect that really exists; a false negative.

In Turkish: II. tip hata, ikinci tür hata, yanlış negatif

How it is used

Its probability is written β (beta), and statistical power equals 1 − β. Small samples make Type II errors more likely, which is why a non-significant result from a small study is weak evidence that there is no effect.

Example sentences

  1. With only 20 participants per group, the risk of a Type II error is high.

  2. A screening test that misses real cases is making a Type II error.

Common collocations

  • Type II error rate
  • commit a Type II error
  • probability of a Type II error
  • false negative
  • type I errorRejecting a null hypothesis that is actually true, in other words concluding that an effect exists when it does not; a false positive.
  • statistical powerThe probability that a test will detect an effect when a real effect of a given size exists; it equals one minus the probability of a Type II error.
  • sample sizeThe number of observations or participants included in a study, usually written as n (or N for the total).
  • null hypothesisThe default claim in a statistical test that there is no effect, no difference or no relationship; the test checks whether the data give enough evidence against it.

More terms: Hypothesis Testing

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