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When a $$p$$ value is greater than the alpha criterion ($$>.05$$), researchers retain the null hypothesis, concluding there is not enough evidence to reject it. Importantly, retaining the null hypothesis is not the same as proving it is true; it simply means the data does not provide sufficient evidence to support the alternative hypothesis. Consequently, researchers use terminology like 'fail to reject the null hypothesis' or 'retain the null hypothesis,' but they never state that they 'accept' the null hypothesis.

Retaining the Null Hypothesis

A Type II error is a false negative in null hypothesis testing. It occurs when a researcher mistakenly concludes that their results are not statistically significant—thereby retaining the null hypothesis—when, in reality, the null hypothesis is false and a true relationship does exist in the population. In practical research, Type II errors typically happen because a study lacks adequate statistical power to detect the underlying relationship, often due to an insufficiently large sample size.

Type II Error

When the data in a study does not provide sufficient evidence to support the alternative hypothesis, which of the following is the correct terminology researchers use to describe their conclusion regarding the null hypothesis?

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