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The false positive rate is equal to the significance level. The specificity of the test is equal to 1 minus the false positive rate. In statistical hypothesis testing, this fraction is given the Greek letter α, and 1 − α is defined as the specificity of the test. Increasing the specificity of the test lowers the probability of type I errors ...
In statistics, when performing multiple comparisons, a false positive ratio (also known as fall-out or false alarm rate [1]) is the probability of falsely rejecting the null hypothesis for a particular test. The false positive rate is calculated as the ratio between the number of negative events wrongly categorized as positive (false positives ...
In statistical hypothesis testing, a type I error, or a false positive, is the rejection of the null hypothesis when it is actually true. A type II error, or a false negative, is the failure to reject a null hypothesis that is actually false. [1] Type I error: an innocent person may be convicted. Type II error: a guilty person may be not convicted.
A test which reliably detects the presence of a condition, resulting in a high number of true positives and low number of false negatives, will have a high sensitivity. This is especially important when the consequence of failing to treat the condition is serious and/or the treatment is very effective and has minimal side effects.
("This is a specific test. Because the result is positive, we can confidently say that the patient has the condition.") See sensitivity and specificity and type I and type II errors for exhaustive definitions. Significance level of a test (α) p-value; Statistical significance test: A predecessor to the statistical hypothesis test (see the ...
In the most basic sense, there are four possible outcomes for a COVID-19 test, whether it’s molecular PCR or rapid antigen: true positive, true negative, false positive, and false negative.
But such an approach is conservative if dependence is actually positive. To give an extreme example, under perfect positive dependence, there is effectively only one test and thus, the FWER is uninflated. Accounting for the dependence structure of the p-values (or of the individual test statistics) produces more powerful procedures. This can be ...
If you've been having trouble with any of the connections or words in Tuesday's puzzle, you're not alone and these hints should definitely help you out. Plus, I'll reveal the answers further down ...