A RCT comparing the efficacy of two drugs showed the difference between the two with a p value of <0.05. In reality the two drugs however do not differ. The difference thus obtained is an example of:
|A||Type 1 error ‘|
|C||Type II error|
The probability of committing type I error is P value. Hence please remember it as a rule: Any question based on p-value, you only have to look at that option that describes a type I error or a false positive error. Any option beginning with type II error or β or false negative error or the power of the study can never be the answer to this question.
Type I Error: That is the rejection of null hypothesis in spite being true
Also called as false positive error i.e. in reality there is no difference but the study has concluded a difference.