MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -You can do this by increasing your sample size and decreasing the number of variants. Interestingly, improving the statistical power to reduce the probability of Type II errors can also be achieved by decreasing the statistical significance threshold, but, in turn, it increases the probability of Type I errors.

Detailed explanation-2: -There is a way, however, to minimize both type I and type II errors. All that is needed is simply to abandon significance testing. If one does not impose an artificial and potentially misleading dichotomous interpretation upon the data, one can reduce all type I and type II errors to zero.

Detailed explanation-3: -You can decrease the possibility of Type I error by reducing the level of significance. The same way you can reduce the probability of a Type II error by increasing the significance level of the test.

Detailed explanation-4: -As the sample size increases, the probability of a Type II error (given a false null hypothesis) decreases, but the maximum probability of a Type I error (given a true null hypothesis) remains alpha by definition.

Detailed explanation-5: -A larger sample size increases the chance of finding statistical differences and the test’s reliability. Increase the degree of significance. Another option is to focus on a smaller number. For instance, a scientist may choose a level of significance of 0.10 rather than the widely accepted criterion of 0.05.