A potential mistake when interpreting the data is to reject the H0 when it is true. This is referred to as

Rejecting the null hypothesis (H0) when it is actually true is known as a type I error. This type of error occurs when a statistical test indicates that there is an effect or a difference when, in reality, none exists. It is a significant concern in hypothesis testing, as it can lead to false conclusions about the effectiveness of a treatment or intervention in healthcare settings.

The significance level, often denoted as alpha (α), defines the threshold for determining whether to reject the null hypothesis. A typical alpha level is set at 0.05, meaning there is a 5% chance of committing a type I error. Understanding this concept is crucial for researchers and statisticians, as it helps in designing studies and interpreting the results accurately, ensuring valid conclusions are drawn from the data.