Falling to reject (accepting) a false null hypothesis.
It depends on whether it is the Type I Error or the Type II Error that is increased.
The power of a test is 1 minus the probability of a Type II error.
A beta error is another term for a type II error, an instance of accepting the null hypothesis when the null hypothesis is false.
In some cases a choice of tests may be available; some tests are more powerful than others.Use a larger sample.There is a trade-off between Type I and Type II errors so you can always reduce the Type I error by allowing the Type II error to increase.
No....the two are mirror images of each other. Reducing type I would increase type II
It depends on whether it is the Type I Error or the Type II Error that is increased.
In hypothesis testing, a Type I error occurs when a true null hypothesis is incorrectly rejected, while a Type II error occurs when a false null hypothesis is not rejected.
It depends on whether it is the Type I Error or the Type II Error that is increased.
made a Type II error.made a Type II error.made a Type II error.made a Type II error.
The power of a test is 1 minus the probability of a Type II error.
Yes.
A beta error is another term for a type II error, an instance of accepting the null hypothesis when the null hypothesis is false.
In some cases a choice of tests may be available; some tests are more powerful than others.Use a larger sample.There is a trade-off between Type I and Type II errors so you can always reduce the Type I error by allowing the Type II error to increase.
No....the two are mirror images of each other. Reducing type I would increase type II
In statistics: type 1 error is when you reject the null hypothesis but it is actually true. Type 2 is when you fail to reject the null hypothesis but it is actually false. Statistical DecisionTrue State of the Null HypothesisH0 TrueH0 FalseReject H0Type I errorCorrectDo not Reject H0CorrectType II error
Accept lower p-values (meaning lower in magnitude; values tending toward zero).--And don't forget that by reducing the probability of getting a type I error, you increase the probability of getting a type II error (inverse relationship).
Failing to reject a false null hypothesis.