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Type I error is the margin of error you can bear (if you are going to purchase the spare parts used in aeroplane you need to assure these should be almost99% accurate the margin of error is reduced to1% rather than the parts used in agricultural machinery say you need to be sure that these should be95% accurate the margin of error is increased to5%). the Type II error is the power of the test that is related to the sample size as the sample size increase the estimates are close to the parametric values and hence are more reliable. the small sample size increases the type II error.
Hi QasiM,
Good afternoon. In my opinion, Type I error is more related to level of significance. If you'll change it from .05 to .01 then there'll be less probability of getting Type I error. Type II error is more related to sample size. If you'll increase the sample size then Type II error will get reduce. To decide the appropriate sample size you should use different tests like, t-test, z-test etc. as per your data requirement. Apart from this, there is one more possibility is wrong recording of data or poor performance of subject. So after studying the data carefully, you can come to final conclusion about your action plan. Whether you should change significance level, sample size or rectify the data or train subject to improve accuracy. I hope I'd explained well. If you do have different opinion kindly share.
