A potential mistake when interpreting the data is to reject the null hypothesis when it is true. What is this known as?

Rejecting the null hypothesis when it is actually true is known as a type I error. This concept is fundamental in hypothesis testing, where the null hypothesis represents a default position or status quo that there is no effect or difference.

In statistical testing, researchers set a significance level (often denoted as alpha, typically 0.05) to determine the threshold for rejecting the null hypothesis. If the p-value obtained from the test is less than this significance level, the null hypothesis is rejected, leading to the potential for a type I error. This is particularly critical in healthcare data, where falsely concluding that a treatment is effective when it is not can have serious implications for patient care and resource allocation.

Understanding type I errors is essential for proper study design and interpretation of results, ensuring that conclusions drawn from statistical analyses are reliable and valid. The other error types—type II errors, which involve failing to reject a false null hypothesis, and type III errors, which pertain to correctly rejecting the null hypothesis for the wrong reason—are related but distinct concepts.