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.
This type of minting error is called 'Off Center'. the coins with nearly all the impression missing are worth more, but those with those with date and mint mark are worth the most. Average worth of a wheat penny with off center error is $3.50.
i operate a cleaning business and need some type of libility insurance to cover and error caused by myself or my employees what would that cost me monthly?
No, it is a type of government
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two ideal type political economies
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.
It is the same as the significance level of the test - often 5%.
The power of a test is 1 minus the probability of a Type II error.
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.
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.
Rejecting a true null hypothesis.
An alpha error is another name in statistics for a type I error, rejecting the null hypothesis when the null hypothesis is true.
A beta error is another term for a type II error, an instance of accepting the null hypothesis when the null hypothesis is false.
Rejecting a true null hypothesis.
No....the two are mirror images of each other. Reducing type I would increase type II
Failing to reject a false null hypothesis.
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.