Which type of error occurs when a null hypothesis is rejected incorrectly?

A Type 1 error occurs when the null hypothesis is rejected when it is, in fact, true. This is often referred to as a "false positive," meaning that the analysis suggests there is an effect or a difference when there is none.

In hypothesis testing, researchers generally start with a null hypothesis that proposes no effect or no difference. A Type 1 error can lead to misleading conclusions, as it indicates the presence of an effect that does not actually exist. The significance level, often denoted by alpha (α), sets the threshold for determining whether to reject the null hypothesis. For example, if a study sets alpha at 0.05, it signifies a 5% risk of committing a Type 1 error, thereby falsely concluding a significant effect.

In contrast, a Type 2 error happens when a null hypothesis is not rejected when it is false, suggesting that a real effect exists but fails to be detected. Statistical error is a broader term that encompasses both type of errors and other potential inaccuracies in data analysis. Measurement error refers specifically to inaccuracies in the data collection process itself and does not directly relate to hypothesis testing. Thus, the correct identification of a Type 1 error highlights the importance of understanding the implications of rejecting a null