0
Failing to reject a false null hypothesis.
Wiki User
∙ 9y ago
Add your answer:
Earn +20 pts
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 a Type II Error - a false-negative error?
Falling to reject (accepting) a false null hypothesis.
What is the probability of making a Type II error if the null hypothesis is actually true?
zero. We have a sample from which a statistic is calculated and will challenge our held belief or "status quo" or null hypothesis. Now you present a case where the null hypothesis is true, so the only possible error we could make is to reject the null hypothesis- a type I error. Hypothesis testing generally sets a criteria for the test statistic to reject Ho or fail to reject Ho, so both type 1 and 2 errors are possible.
What factor can researcher control that will reduce the risk of a type 1 error?
In statistical tests there are 2 main types of Errors, Type I and Type II. Type 1 errors occur when you reject a null hypothesis that is actually true and is thus refereed to as a false positive. Type II errors are essentially the opposite, accepting a null hypothesis that is false, and is often called a false negative. You can reduce the risk of a type I error by lowering the value of P that you're significance test must return to reject the null, but doing so will increase the chance of a type II error. The only way to reduce both is to increase the entire sample size. Alternatively, in some cases, it may also be possible to lower the standard deviation of the experiment, which would also decrease the risk of type I and type II errors.
What happens to the confidence interval if you increase the margin of error?
It depends on whether it is the Type I Error or the Type II Error that is increased.
When a researcher fails to reject Null Hypothesis when Null Hypothesis is false he has?
made a Type II error.made a Type II error.made a Type II error.made a Type II error.
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.
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 a Type II Error - a false-negative error?
Falling to reject (accepting) a false null hypothesis.
What is the probability of making a Type II error if the null hypothesis is actually true?
zero. We have a sample from which a statistic is calculated and will challenge our held belief or "status quo" or null hypothesis. Now you present a case where the null hypothesis is true, so the only possible error we could make is to reject the null hypothesis- a type I error. Hypothesis testing generally sets a criteria for the test statistic to reject Ho or fail to reject Ho, so both type 1 and 2 errors are possible.
Type 1 error and type 2 error?
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
When performing a study what is a type 1 error?
A Type I error is committed whenever a true null hypothesis is rejected. A Type II error is committed whenever a false null hypothesis is accepted. The best way to explain this is by an example. Suppose a company develops a new drug. The FDA has to decide whether or not the new drug is safe. The null hypothesis here is that the new drug is not safe. A Type I error is committed when a true null hypothesis is rejected, e.g. the FDA concludes that the new drug is safe when it is not. A Type II error occurs whenever a false null hypothesis is accepted, e.g. the drug is declared unsafe, when in fact it is safe. Hope this helps.
Can a hypothesis still be right even if the experiment says it is wrong?
Yes, although if the experiment is performed correctly there should be relatively little chance of this occurring. This is referred to as a type II error in statistics - the data supports rejecting the hypothesis even though the hypothesis is correct.
What factor can researcher control that will reduce the risk of a type 1 error?
In statistical tests there are 2 main types of Errors, Type I and Type II. Type 1 errors occur when you reject a null hypothesis that is actually true and is thus refereed to as a false positive. Type II errors are essentially the opposite, accepting a null hypothesis that is false, and is often called a false negative. You can reduce the risk of a type I error by lowering the value of P that you're significance test must return to reject the null, but doing so will increase the chance of a type II error. The only way to reduce both is to increase the entire sample size. Alternatively, in some cases, it may also be possible to lower the standard deviation of the experiment, which would also decrease the risk of type I and type II errors.
What happens to the confidence interval if you increase the margin of error?
It depends on whether it is the Type I Error or the Type II Error that is increased.
Is there a direct relationship between the power of a test and the probability of a Type II error?
The power of a test is 1 minus the probability of a Type II error.
Does increasing the margin of error decrease the length of the confidence interval?
It depends on whether it is the Type I Error or the Type II Error that is increased.
