A randomized trial comparing the efficacy of two drugs showed a difference between the two (p<0.05). Assume that in reality, however, the two drugs to not differ. This is therefore an example of:-
STATISTICAL ERRORS & P - VALUE H0 rejected H0 not rejected Null hypothesis (H0) true Null hypothesis (H0) false Type I error No error No error Type II error Type I error: Null hypothesis is true but rejected. Probability of type I error is given by 'P value'. (probability of declaring a significant difference when actually it is not present) Significance (alpha) level: is the maximum tolerable probability of type I error. Alpha is fixed in advance: p - value calculated can be less than, equal to or greater than alpha. Keep type I error to be minimum (P<alpha); Then results are declared statistically significant. Type II error: Null hypothesis is false but not rejected. (or accepted) Probability of Type II error is given by beta. (probability of declaring no significant difference when actually it is present) Type I error is more serious than type II error.
Type I error
Type II error
1 - alpha
1 - beta
Biostats