# Probability

« Back to Glossary Index## Probability

Quantitative estimate of the likelihood of a condition existing (as in diagnosis) or of subsequent events (such as in an intervention study).

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- Sensitivity AnalysisAny test of the stability of the conclusions of a health care evaluation over a range of probability estimates, value judgments, and assumptions about the structure of the decisions to be made. This may involve the repeated evaluation of a decision model in which one or more of the parameters of interest are varied.
- Likelihood RatioFor a screening or diagnostic test (including clinical signs or symptoms), the LR expresses the relative likelihood that a given test would be expected in a patient with, as opposed to one without, a disorder of interest. A likelihood ratio of 1 means that the post-test probability is identical to the pre-test probability. As likelihood ratios increase above 1, the post-test probability progressively increases in relation to the pre-test probability. As likelihood ratios decrease below 1, the post-test probability progressively decreases in relation to the pre-test probability. A likelihood ratio is calculated as the proportion of target positive with a particular test result (which, with a single cut point, would be either a positive or negative result) divided by the proportion of target negative with same test result.
- Cochrane’s QA common test for heterogeneity that assumes the null hypothesis that all the apparent variability between individual study results is due to chance. Cochrane’s Q generates a probability, presented as a p-value, based on a Chi-square distribution, that between-study differences in results equal to or greater than those observed are likely to occur simply by chance.
- Law of Multiplicative ProbabilitiesThe law of multiplicative probabilities for independent events (where one event in no way influences the other) tells us that the probability of 10 consecutive heads in ten coin flips can be found by multiplying the probability of a single head ½ 10 times over; that is, 1/2 × 1/2 × 1/2, and so on.
- P-valueThe probability that results as or more extreme than those observed would occur if the null hypothesis was true and the experiment was repeated over and over. A P value < 0.05 means that there is a less than 1 in 20 probability of that, on repeated performance of the experiment, the results as or more extreme than those observed would occur if the null hypothesis were true.

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