A Type I Error occurs in statistical hypothesis testing when a researcher incorrectly rejects a true null hypothesis. This means that the researcher concludes there is an effect or a difference when, in fact, none exists. For example, if a medical test indicates a patient has a disease when they do not, this is a Type I Error. The probability of making a Type I Error is denoted by the symbol alpha (α), which is often set at a threshold, such as 0.05. This threshold indicates a 5% risk of concluding that a difference exists when it actually does not. Understanding Type I Errors is crucial in fields like medicine and social sciences to ensure accurate results.