A Type I error, also known as a false positive or an error of the first kind, occurs when a true null hypothesis is incorrectly rejected in a statistical test. In other words, it means concluding that there is a significant effect or difference when in reality there is none

. This error represents mistakenly detecting an effect that does not exist. For example, in a courtroom analogy, a Type I error corresponds to convicting an innocent person

. In scientific experiments or A/B testing, it means declaring a variation or treatment effective when it actually isn't, leading to false conclusions and potentially wasted resources

. The probability of making a Type I error is denoted by the significance level, alpha (α), which is typically set at 0.05 (5%). This means there is a 5% chance of rejecting the null hypothesis when it is actually true

. Lowering the significance level reduces the risk of a Type I error but may increase the risk of a Type II error (failing to detect a true effect)

. In summary, a Type I error is the incorrect rejection of a true null hypothesis, leading to a false positive conclusion in hypothesis testing