A Type I error occurs when a researcher incorrectly rejects a true null hypothesis. This means that the researcher concludes there is an effect or difference when, in reality, none exists. For example, if a medical test indicates a patient has a disease when they do not, this is a Type I error. In statistical terms, the probability of making a Type I error is denoted by the Greek letter alpha (α). Researchers often set a significance level, commonly at 0.05, which indicates a 5% risk of committing a Type I error in their findings.