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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.
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If a test of hypothesis has a type I error probability of 0.01 it means?
It means that, if the null hypothesis is true, there is still a 1% chance that the outcome is so extreme that the null hypothesis is rejected.
What is the probability of making type 1 error when null hypothesis is true?
It is the same as the significance level of the test - often 5%.
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.
What does the p value have to be for you to reject it in chi square conclusions?
The p value for rejecting an hypothesis is more closely related to the type of errors and their consequences. The p value is not determined by the chi square - or any other - test but by the impact of the decision made on the basis of the test. The two types of errors to be considered are: what is the probability that you reject the null hypothesis when it is actually true (type I error), and what is the probability that you accept the null hypothesis when, in fact, it is false (type I error).. Reducing one type of error increase the other and there is a balance to be struck between the two. This balance will be influenced by the costs associated with making the wrong error. In real life, the effects (costs/benefits) of decisions are very asymmetrical.
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 is Hypothesis Testing of Type I Error?
Rejecting a true null hypothesis.
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.
What is Type I Error - a false-positive error?
Rejecting a true null hypothesis.
If the probability of type 1 error is reduced the probability of type 2 error is also reduced?
No....the two are mirror images of each other. Reducing type I would increase type II
What is Hypothesis Testing of Type II Error?
Failing to reject a false null hypothesis.
Relationship between type 1 error and type 2 error?
In statistics, there are two types of errors for hypothesis tests: Type 1 error and Type 2 error. Type 1 error is when the null hypothesis is rejected, but actually true. It is often called alpha. An example of Type 1 error would be a "false positive" for a disease. Type 2 error is when the null hypothesis is not rejected, but actually false. It is often called beta. An example of Type 2 error would be a "false negative" for a disease. Type 1 error and Type 2 error have an inverse relationship. The larger the Type 1 error is, the smaller the Type 2 error is. The smaller the Type 2 error is, the larger the Type 2 error is. Type 1 error and Type 2 error both can be reduced if the sample size is increased.
