A randomized trial comparing efficacy of two regimens showed that difference is satisfically significant with p <0.001 but in reality the two drugs do not differ in their efficacy. This is an example of ?
Ans. is 'a' i.e., Type I error (a-error) o Type I error --> we find difference when in reality it is not there, i.e., false positive difference. o Type II error ----> We do not find difference when in reality it is there, i.e., false negative. Relation of sensitivity and specificity to error o If a test is more sensitive, there will be less false negative, Thus by making test more sensitive, type II error can be reduced. False negative rate (a) = 1-Sensitivity o If a test is more specific, false positive rate will be less. Thus making a test more specific reduces Type I error. False positive rate ((3) = 1 - Specificity. Also know o /3 error determines the power of the study Power of a test = 1 -- (3 error. o Power of the study - It is the ability of the statistical test to detect a null hypothesis that is false. o It is the probability that a false null hypothesis will be rejected. o Conventionally a study is required to have a power of 0.8 to be acceptable i.e. a study that has less than 80% chance of detecting a false null hypothesis is unacceptable.
Type - I error (a error)
Type-II error (II error)
I-a
I-b