Question

A new diagnostics test is proposed for COVID19. this test correctly identifies infected people 99% of the times and identifies uninfected people 80% of the times. Then, Type I error and Type I error for this test is ____ and ____ respectively.

Answer 1

Type 1 error = A Type I error (or Type 1), is the incorrect rejection of a true null hypothesis

in our case null hypothesis will be that people tested are infected and 99% of times test will correctly diagnose or we will accept our null hypothesis when it is actually true for the 99 % of the times

but for the 1% of the times test will not identify the infected people as infected that is it will reject our null hypothesis when it is actually true we can identify this as false positive or type 1 error

type 1 error - 1%

type 2 error - A Type II error (sometimes called a Type 2 error) is the failure to reject a false null hypothesis

now in our case null hypothesis is that the people tested are infected , now for the 80% of the times the test will reject our null hypothesis when it is actually false that is people are really not infected but for the rest of 20% of the times it will fail to reject the null hypothesis when it is actually false that is it will diagonse people as infected when they are really not infected

type 2 error = 20%