What causes a type II error in a hypothesis test?

A type II error occurs in statistical inference when a researcher fails to reject the null hypothesis even though it is actually false.1

Because hypothesis testing relies on data from a sample rather than the entire population, there is an inherent risk of error. A type II error—denoted by the probability $\beta$—essentially represents a "false negative," where the test fails to detect an effect or difference that truly exists.12

While researchers typically control the probability of a type I error (rejecting a true null hypothesis) by setting a level of significance ($\alpha$), they do not always control the probability of a type II error. Factors such as sample size directly influence this probability; for instance, an operating-characteristic curve can be used to visualize how increasing the sample size can help reduce the likelihood of committing a type II error.1