The probability of a Type II error is3/20 = 15%.
The following Buzzle article will explain to you the difference between type 1 and type 2 errors with examples.
A Type I error can only occur if anull hypothesis,H
Mickey UCLA True/FalseTYPE1ERROR TESTING CONCEPT STATISTICST= 2 ComprehensionD= 3 GeneralBack to 14891Back to 14901
This documentation refers to analyses when simply as iterative influence analysis, even if final covariance parameter estimates can be updated in a single step (for example, when MIVQUE0 or TYPE3). This nomenclature reflects the fact that only if are all model parameters updated, which can require additional iterations. If and REML (default) or ML, the procedure updates fixed effects variancecovariance parameters after removing the selected observations with additional NewtonRaphson iterations, starting from the converged estimates for the entire data. The process stops for each observation or set of observations if the convergence criterion is satisfied or the number of further iterations exceeds . If > 0 and TYPE1, TYPE2, or TYPE3, ANOVA estimates of the covariance parameters are recomputed in a single step.
A Type II error can only occur if a nullhypothesis,H
Type I and Type II errors are two wellknown concepts in quality engineering, which are related to hypothesis testing. Often engineers are confused by these two concepts simply because they have many different names. We list a few of them here.
All of which is to say: of course the FDA errs more on type 2's. And the growing pharmacopeia (much of which is generic and therefore low cost) only encourages this bias towards type 2 errors, particularly in regard to followon drugs (i.e., new molecular entities in the same class as approved drugs). If — the FDA figures, albeit not publicly
– by instinct, as it were — there is already a good drug that helps a majority of people why take the chance that a second drug in the same class will provide more incremental benefit than incremental risk, which — as I note above — comes with disproportionate institutional costs?
The power of a test is 1  probability(Type IIerror).
Bugbee  UNH True/FalseTESTOFSIGNIFICAN TYPE1ERROR CONFIDENCEINTERV CONCEPT STATISTICS ESTIMATIONT= 2 ComprehensionD= 1 GeneralBack to 16614
Shavelson  UCLA Multiple ChoiceTESTOFSIGNIFICAN TYPE1ERROR BASICTERMS/STATS CONCEPT STATISTICST= 2 ComprehensionD= 3 GeneralBack to 16171
Figure 1: Illustration of Type I and Type II Errors

type 1 errorBack to Back to 16622
type 2 errors comparison outlined below along with suitable examples will help you understand this concept better.

trade off between type 1 and type 2 errors ..
Type I and Type II errors can be defined in terms of hypothesis testing.

Type 1 and type II errors are mistakes in testing a hypothesis
Using a sample size of 16 and the critical failure number of 0, the Type I error can be calculated as:
What are the differences between Type1 errors and Type 2 ..
is used to increase the level of confidence, which in turn reduces Type I errors. The chances of making a Type I error are reduced by increasing the level of confidence that the event A and measurement B are within our control and are not being caused by chance or some other external events. This results in more stringent criteria for rejecting the null hypothesis (such as specific pass/fail criteria), – (failing to reject H0 when it was really false and should have been rejected)!
Questions on Type 1 and Type 2 error  Talk Stats Forum
A statement that opposes this statement can be termed as 'alternative hypothesis'.
The acceptance and rejection of the null hypothesis is done by means of the type 1 and type 2 errors.
Type 1 and Type 2 Errors  SAGE Research Methods
Or, a drug has cured a particular disease, but is not accepted to be effective against the same.
Both, type 1 and type 2 errors are important and need to be taken into consideration in all fields, especially while calculating them in the fields of mathematics and science.
Difference between Type 1 and Type 2 Errors With …
requests that chisquare tests be performed for all specified effects in addition to the F tests. Type 3 tests are the default; you can produce the Type 1 and Type 2 tests by using the option.
A Type I error can only occur if a null hypothesis,H o, is true
A Type I error (sometimes called a Type 1 error), is the incorrect rejection of a true null hypothesis. The alpha symbol, α, is usually used to denote a Type I error.
Type 1 errors and Type 2 errors ..
You conduct your research by polling local residents at a retirement community and to your surprise you find out that most people do believe in urban legends. The problem is, you didn’t account for the fact that your sampling method introduced some bias…retired folks are less likely to have access to tools like Smartphones than the general population. So you incorrectly fail to reject the false null hypothesis that most people do believe in urban legends (in other words, most people do not, and you failed to prove that). You’ve committed an egregious Type II error, the penalty for which is banishment from the scientific community.