Final answer:
A positive likelihood ratio greater than 10 means it is likely that the individual actually has the disease, indicating the test is good at ruling the disorder in. The likelihood ratio diminishes the chance of a false positive, reassured by the test's specificity.
Step-by-step explanation:
A positive likelihood ratio greater than 10 suggests that a positive test is good at ruling the disorder in. This means that if a diagnostic test gives a positive result, it's highly likely that the individual actually has the disease. A high likelihood ratio reduces the chances of a false positive, where the test indicates the presence of disease when it is not actually present.
To further understand, let's consider a test with a Type I error, also known as an α level, set at 10%. This indicates that there is a 10% chance of detecting the disease (like TB) when it is indeed not present (false positive). Concurrently, a Type II error (or β level) at 20% implies that there's a 20% probability the test will miss the disease when it is actually there (false negative).
Test specificity is the probability that a diagnostic test will not find evidence of the targeted disease when the disease is absent, further securing the likelihood that a positive test means the disease is actually present. In making a diagnosis, all the evidence from preliminary tests contributes to reaching the best possible conclusion regarding the patient's health condition.