Search results
Results from the WOW.Com Content Network
The positive predictive value (PPV), or precision, is defined as = + = where a "true positive" is the event that the test makes a positive prediction, and the subject has a positive result under the gold standard, and a "false positive" is the event that the test makes a positive prediction, and the subject has a negative result under the gold standard.
A positive result in a test with high specificity can be useful for "ruling in" disease, since the test rarely gives positive results in healthy patients. [5] A test with 100% specificity will recognize all patients without the disease by testing negative, so a positive test result would definitively rule in the presence of the disease. However ...
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 ...
The pre-test probability of an individual can be chosen as one of the following: The prevalence of the disease, which may have to be chosen if no other characteristic is known for the individual, or it can be chosen for ease of calculation even if other characteristics are known although such omission may cause inaccurate results
In fact, post-test probability, as estimated from the likelihood ratio and pre-test probability, is generally more accurate than if estimated from the positive predictive value of the test, if the tested individual has a different pre-test probability than what is the prevalence of that condition in the population.
This combined with the definition of conditional probability results in the above statement. In other words, if someone tests positive, the probability that they are a cannabis user is only 19%—because in this group, only 5% of people are users, and most positives are false positives coming from the remaining 95%.
A test statistic is the output of a scalar function of all the observations. This statistic provides a single number, such as a t-statistic or an F-statistic. As such, the test statistic follows a distribution determined by the function used to define that test statistic and the distribution of the input observational data.
The probability that an observed positive result is a false positive may be calculated using Bayes' theorem. False negatives produce serious and counter-intuitive problems, especially when the condition being searched for is common. If a test with a false negative rate of only 10% is used to test a population with a true occurrence rate of 70% ...