Which statistical error occurs when a research study wrongly rejects the null hypothesis?

A Type I error occurs when a research study incorrectly rejects the null hypothesis, which essentially means that the study finds evidence for an effect or difference when, in reality, none exists. This error is often referred to as a "false positive." When conducting hypothesis testing, researchers set a significance level (typically alpha = 0.05) which acts as a threshold for determining whether to reject the null hypothesis. If the findings of a study yield a p-value less than this significance level, the null hypothesis is rejected. However, if this rejection is made without true evidence supporting the alternative hypothesis, a Type I error has occurred.

Understanding this error is crucial for cardiovascular pharmacists and other healthcare professionals, as it highlights the importance of critical appraisal of research findings, ensuring that treatment decisions are based on sound evidence. This error emphasizes that just because a study finds statistical significance does not guarantee that it reflects a true effect or difference in clinical practice.

In contrast, a Type II error involves failing to reject the null hypothesis when it is actually false, which is more about missing a real effect. Reporting bias and selection bias pertain to the integrity of study design and data collection rather than errors in hypothesis testing.