**Question:** A randomized trial comparing the efficacy of two drugs showed a difference between the two (p value < 0.005). However, in reality the two drugs do not differ. This is an example of- A. Type I error B. Type II error C. Confirmatory research D. Quasi-experimental design **Core Concept:** A statistical hypothesis test, such as a t-test or ANOVA, is performed to determine if there is a significant difference between the means of two or more groups. The significance level (alpha, denoted as α) is a predetermined probability level used to determine the threshold for rejecting the null hypothesis. In this case, we have a p-value of <0.005, which is less than the predetermined significance level. **Why the Correct Answer is Right:** In this scenario, we are discussing a situation where the calculated p-value (significance level) is less than the predetermined alpha value. This is known as a Type I error, which occurs when we reject the null hypothesis (H0), stating that there is a difference between the groups, when in fact there is no difference. The p-value is lower than the chosen significance level, leading to a false positive result. **Why Each Wrong Option is Incorrect:** A. Type II error (B): This error occurs when we fail to reject the null hypothesis (H0) when there is a true difference between the groups. In this scenario, the p-value is not lower than the significance level, so Type II error is not applicable. B. Confirmatory research (C): This refers to research conducted to prove or disprove a specific hypothesis or theory. In this case, the research is primarily exploratory, aiming to identify differences between the groups, not confirm a pre-existing hypothesis. D. Quasi-experimental design (D): This term refers to studies where the allocation of participants to groups is not randomized, making the study less robust and less generalizable than a randomized trial. In this scenario, the study is randomized, so this option is not relevant. **Clinical Pearls:** 1. In hypothesis testing, a p-value less than the significance level (α) indicates that the null hypothesis (H0) is likely to be rejected. Although the study shows a difference between the groups, the difference is not significant due to the p-value being higher than α (0.05 in this case). 2. The randomized trial design allows for a high degree of internal validity, as the allocation of participants to groups is random and independent of any confounding variables. This ensures that the observed difference between groups is due to the intervention, not due to chance or other confounding factors. 3. In this situation, the p-value is not less than the significance level, suggesting a Type I error (false positive result). This means that the observed difference between the groups is likely due to random chance, not real effect size.