What happens to the Type II error rate, as we reduce the Type I error rate? Why?
ID: 3329085 • Letter: W
Question
What happens to the Type II error rate, as we reduce the Type I error rate? Why? Similarly, what happens to the Type I error rate as the Type II error rate is reduced? Why?What would we need to do, in order to reduce the Type I/II error rate to 0?What happens to the Type II error rate, as we reduce the Type I error rate? Why? Similarly, what happens to the Type I error rate as the Type II error rate is reduced? Why?What would we need to do, in order to reduce the Type I/II error rate to 0?
What happens to the Type II error rate, as we reduce the Type I error rate? Why? Similarly, what happens to the Type I error rate as the Type II error rate is reduced? Why?What would we need to do, in order to reduce the Type I/II error rate to 0?
Explanation / Answer
The probability of type 2 error (beta) will increase if we decrease type 1 error (alpha). It depends on what is the true answer of the unknown parameter you're testing. In other words, beta is a function of the unknown parameter.
Type I error is the chance of rejecting the true sample. That is we reject the null hypothesis when its actually is true at a given level of significance. The alpha is the significance level which is the probability of committing the type I error.
Type II errors happen when we fail to reject a false null hypothesis.
Type I and type II errors are part of the process of hypothesis testing. Although the errors cannot be completely eliminated, we can minimize one type of error.
Typically when we try to decrease the probability one type of error, the probability for the other type increases.
We could decrease the value of alpha from 0.05 to 0.01, corresponding to a 99% level of confidence. However, if everything else remains the same, then the probability of a type II error will nearly always increase.
If is set at 0.10, then the investigator has decided that he is willing to accept a 10% chance of missing an association of a given effect size between Tamiflu and psychosis. This represents a power of 0.90, i.e., a 90% chance of finding an association of that size.
Ideally alpha and beta errors would be set at zero, eliminating the possibility of false-positive and false-negative results. In practice they are made as small as possible. Reducing them, however, usually requires increasing the sample size.
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