In hypothesis testing, what is known as Type I error?

In hypothesis testing, a Type I error is defined as the incorrect decision to reject the null hypothesis (H0) when it is, in fact, true. This means that the test concludes there is an effect or a difference when there actually is none. The significance level of a hypothesis test (often denoted as alpha, α) represents the probability of making a Type I error. If a researcher sets a significance level of 0.05, for instance, they are accepting a 5% chance of incorrectly rejecting the true null hypothesis.

Understanding Type I error is crucial for interpreting the results of a hypothesis test and assessing the reliability of statistical conclusions. It underscores the importance of carefully considering the implications of a false positive result in research and decision-making processes.