Type I Error

What is a Type I Error?

A Type I error, also known as a false positive error, occurs in hypothesis testing when the null hypothesis (H0) is incorrectly rejected when it is true.

In other words, a Type I error is the incorrect rejection of a true null hypothesis, leading to the conclusion that there is a significant effect or difference when there is no such effect or difference.

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 I error occurs when the null hypothesis is incorrectly rejected in favor of the alternative hypothesis, even though the null hypothesis is true.

Significance Level (Alpha)

The significance level (α) is the probability of committing a Type I error. It is typically set before conducting the hypothesis test and is denoted by a value such as 0.05 (5%) or 0.01 (1%).
A smaller significance level reduces the chance of Type I error but increases the risk of Type II error (false negative error).

Consequences

Type I errors can lead to incorrect conclusions and decisions. For example, falsely concluding that a treatment is effective when it is not can lead to unnecessary costs, risks, and ineffective treatments.

Controlling Type I Error

Researchers can control Type I errors by choosing an appropriate significance level, conducting power analysis to determine sample size, using multiple comparison adjustments in case of multiple tests, and ensuring the reliability and validity of data and measurements.

Type I Error Examples

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 with a significance level of 0.05 and rejects the null hypothesis based on the sample data, concluding that the drug is effective, but in reality, the drug has no effect, this would be a Type I error.