Type II Error

What is a Type II Error?

A Type II error, also known as a false negative error, occurs in hypothesis testing when the null hypothesis (H0) is not rejected when it is false.

In other words, a Type II error is the failure to reject a false null hypothesis, leading to the conclusion that there is no significant effect or difference when there is such an effect or difference in reality.

Hypothesis Testing

In hypothesis testing, researchers set up a null hypothesis (H0) and an alternative hypothesis (H1). The null hypothesis typically represents the absence of an effect, relationship, or difference, while the alternative hypothesis represents the presence of such an effect, relationship, or difference.

Type II error occurs when the null hypothesis is not rejected even though it is false, leading to a failure to detect a true effect or difference

Probability of Type II Error

The probability of committing a Type II error is denoted by β (beta). It is influenced by factors such as the sample size, effect size, variability of data, and chosen significance level (α).

The probability of Type II error decreases as the sample size increases or the effect size increases. Conversely, a larger variability of data or a smaller significance level increases the probability of Type II error.

Consequences

Type II errors can lead to missed opportunities to detect important effects or differences. For example, failing to detect a treatment’s effectiveness when it is effective can result in missed therapeutic benefits and opportunities for improvement.

Balancing Type I and Type II Errors

Researchers must consider Type I and Type II errors when designing hypothesis tests and interpreting results. Balancing the risks of these errors involves choosing appropriate sample sizes, effect sizes, significance levels, and statistical power to achieve reliable and valid conclusions.

Type II Error Example

Suppose a medical researcher is testing a new drug’s effectiveness in treating a disease. The null hypothesis (H0) states that the drug has no effect, while the alternative hypothesis (H1) states the drug is effective.

If the researcher conducts a hypothesis test but fails to reject the null hypothesis, concluding that the drug is not effective, even though the drug does have an effect, this would be a Type II error.