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What is Hypothesis Testing of Type II Error?
Failing to reject a false null hypothesis.
What is a beta 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.
If the null hypothesis is true and the researchers do not reject it then a correct decision has been made?
True because the point of the hypothesis test is to figure out the probability of the null hypothesis being true or false. If it is tested and it is true, then you do not reject but you reject it, when it is false.
What is a Type II Error - a false-negative error?
Falling to reject (accepting) a false null hypothesis.
If a researcher fails to reject the null hypothesis and the null hypothesis is not true has the researcher made a correct decision a Type I error or Type II error?
If a researcher fails to reject the null hypothesis when it is actually false, they have made a Type II error. This occurs when the researcher incorrectly concludes that there is not enough evidence to support an alternative hypothesis, despite it being true. In contrast, a Type I error happens when the null hypothesis is rejected when it is actually true.
What is Hypothesis Testing of Power?
The probability of correctly detecting a false null hypothesis.
What is Hypothesis Testing of Beta Value?
Probability of failing to reject a false null hypothesis.
What is Hypothesis Testing of Type II Error?
Failing to reject a false null hypothesis.
What is a beta 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.
What is the Probabilities of Beta Value?
In hypothesis testing, this is the probability of failing to reject a false null hypothesis.
If the null hypothesis is true and the researchers do not reject it then a correct decision has been made?
True because the point of the hypothesis test is to figure out the probability of the null hypothesis being true or false. If it is tested and it is true, then you do not reject but you reject it, when it is false.
Types of Hypothesis and definition?
In formal design and analysis of experiments there are but two types of hypotheses: null and alternative. And one might argue there really is only one because when the null is properly defined, the alternative is automatically properly defined. The null hypothesis is a testable statement of conjecture. The purpose of the null hypothesis is to set the measurable goal for the experiment that follows to show that the null is not false. If the results of the experiment do not show that then the alternative hypothesis is by definition not false. Simple Example: Null: It's raining outside. Alt: It's NOT raining outside. NOTE: The NOT reverses the logic of the null. The experiment...walk outside. The test...if I get wet, the Null is not false. If I don't get wet, the alternative is not false. NOTE: I must have an experiment to test the hypothesis. Without a test it's not a valid hypothesis.
What is a Type II Error - a false-negative error?
Falling to reject (accepting) a false null hypothesis.
What is Type I Error - a false-positive error?
Rejecting a true null hypothesis.
How many types of errors in taking a decision about Ho?
There are two types of errors associated with hypothesis testing. Type I error occurs when the null hypothesis is rejected when it is true. Type II error occurs when the null hypothesis is not rejected when it is false. H0 is referred to as the null hypothesis and Ha (or H1) is referred to as the alternative hypothesis.
If a test of hypothesis has a type 1 error probability 01?
If the type 1 error has a probability of 01 = 1, then you will always reject the null hypothesis (false positive) - even when the evidence is wholly consistent with the null hypothesis.
What is the difference between a Type I error and a Type II error in hypothesis testing?
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.
