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What is an alpha error?
An alpha error is another name in statistics for a type I error, rejecting the null hypothesis when the null hypothesis is true.
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.
Null Null Null?
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What is Type I Error - a false-positive error?
Rejecting a true null hypothesis.
Solution to null pointer assignment error?
Answer#ifndef NULL# define NULL ((void*)0)#endifAnswerDon't use pointers that contain NULL-value. Eg:int *myptr= NULL;...*myptr = 32; /* wrong */
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
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.
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 a Type II Error - a false-negative error?
Falling to reject (accepting) a false null hypothesis.
What is the meaning of critical ratio in statistics?
The critical ratio in statistics is a measure used to determine the significance of a test statistic in hypothesis testing. It is typically calculated as the ratio of the difference between the sample mean and the population mean to the standard error of the sample mean. A high critical ratio indicates that the sample mean is far from the population mean, suggesting that the null hypothesis may be rejected. This concept is commonly applied in contexts such as t-tests and z-tests to assess the likelihood of observing the sample data under the null hypothesis.
What is Hypothesis Testing of Type I Error?
Rejecting a true null hypothesis.
Changing the alpha level to .05 from .01 what does it do to the risk of Type 1 error?
This will reduce the type 1 error. Since type 1 error is rejecting the null hypothesis when it is true, decreasing alpha (or p value) decreases the risk of rejecting the null hypothesis.
