of error that may threaten the validity of a study by producing results that are systematically different from the true results. two main categories of bias in epidemiologic studies are selection bias, which occurs when study subjects are selected as a result of another unmeasured variable that is associated with both the exposure and outcome of interest; and information bias, which is systematic error in the assessment of a variable. while useful when assessing a previously unexplored association, there is no combination or minimal number of these criteria that must be met in order to conclude that a plausible relationship exists between a known exposure and an observed outcome. in many fe investigations there is no need for a causal plausibility analysis if a general causal relationship is well established. in large part, plausibility of a relationship is entertained once implausibility has been rejected. the two remaining hill criteria are temporality and strength of association. while both criteria have utility in assessing specific causation, temporality is the feature of an association that must be present, at least with regard to sequence (i.e., the exposure must precede the outcome), in order to consider a relationship causal. temporal proximity can also be useful in some specific causation evaluations, as the closer the investigated exposure and the outcome are in time the less opportunity there is for an intervening cause to act. another feature of temporality that may have a role in a specific causation evaluation is latency. an outcome may occur too soon or too long after an exposure to be considered causally related. as an example, some food borne illnesses must incubate for hours or days after ingestion, and thus an illness that begins directly following a meal, and which is later found to be caused by a food borne microorganism that requires >12 h incubation, was not caused by the investigated meal, even if an investigation were to reveal the microorganism in the ingested food. strength of association is the criterion that is used in general causation to assess the impact of the exposure on the population, and is often quantified in terms of rr. in a specific causation evaluation the strength of the association between the exposure and the outcome is quantified by the crr, as described above. a contingency table, also called a crosstabulation, of possible test outcomes, and the associated equations for evaluating test accuracy. test accuracy test accuracy investigation is a standard practice in clinical epidemiology. in this setting, a diagnostic test is scrutinized to determine by various measures how often a test result is correct. in fe the same principles are used to evaluate the accuracy of proposed tests leading to conclusions that are central to fact finder determinations of guilt or innocence in criminal investigations, and of causality in civil matters. the utility of a test is highly dependent on its accuracy, which is determined by a measure of how often a positive or negative test result truly represents the actual status that is being tested. for any test or criterion there are typically four possible results: (1) a true positive (tp), in which the test correctly identifies tested subjects with the condition of interest; (2) a true negative (tn), in which the test correctly identifies test subjects who do not have the condition of interest; (3) a false positive (fp), in which the test is positive even though condition is not present, and; (4) a false negative (fn) in which the test is negative even though the condition is present. fig. 3.19 is a contingency table illustrating the relationships between test results and condition presence, as well as the following test accuracy parameters: sensitivity (the rate at which the test is positive when the condition is present) tp/(tp + fn) specificity (the rate at which the test is negative when the condition is absent) tn/(tn + fp) positive predictive value (the rate at which the condition is present when the test is positive) tp/(tp + fp) negative predictive value (the rate at which the condition is absent when the test is negative) tn/(tn + fn) bayesian reasoning probability is used to characterize the degree of belief in the truth of an assertion. the basis for such a belief can be a physical system that produces outcomes at a rate that is uniform over time, such as a gaming device like a roulette wheel or a die. with such a system, the observer does not influence the outcome; a fair six-sided die that is rolled enough times will land on any one of its sides 1/6th of the time. an assertion of a probability based in a physical system is easily tested with sufficient randomized experimentation. conversely, the basis for a high degree of belief in an asserted claim may be a personally held perspective that cannot be tested. this does not mean that the assertion is any less true than one that can be tested. as an example, one might truthfully assert that “if i eat a banana there is a high probability that it will make me nauseous” based upon experience unknown to anyone but one’s self. it is difficult to test such assertions, which are evaluated through collateral evidence of plausibility and analogy, often based on similar personal experience. in forensic settings, assertions of belief are often characterized as probabilities, that is, what is most likely, for a given set of facts. for circumstances in which a variety of conditions exist that may modify or “ condition” the probability of a particular outcome or scenario, a method of quantifying the relationship between the modifying conditions and the probability of the outcome employs bayesian reasoning, named for bayes’ theorem or law upon which the approach is based. most simply stated, bayes’ law allows for a more precise quantification of the uncertainty in a given probability. as applied in a forensic setting, bayes’ law tells us what we want to know given what we do know. although bayes’ law is known in forensic sciences primarily for its application to dna evidence, a number of authors have described the use of bayesian reasoning for other applications in forensic medicine, including identification and age estimation. post-test probability the post-test probability is a highly useful bayesian equation that allows for the calculation of the probability that a condition is present when the test is positive, conditioned by the pretest prevalence of the condition of interest. this equation is given in box to the right: post-test probability equation the equation results in a positive predictive value for a given pre-event or pretest prevalence. in a circumstance in which the pretest prevalence is considered “indifferent” the prevalence and (1-prevalence) values cancel out, and the calculation is a simplified to a positive predictive value. examples of investigative questions what is likelihood that the asbestos exposure that mr x experienced during his employment at company z caused his lung cancer? how likely is it that the dna found on the forensic scene belongs to mr x? what is the chance that you are wrong? could you in your probability calculation take into account the other evidence that points towards the identification of mr x? could you estimate the probability that the leg amputation of mrs y could have been prevented if the delay in diagnosis would not have occurred? how likely is it that the heart failure of mrs y was indeed caused by the side effect of this drug? what is the chance that the death that followed the administration of the opiate by 20 minutes was due to the drug and not to other (unknown) factors? what is the chance that mr. x would have needed neck surgery when he did if he had not been in a minor traffic crash the prior month? how likely is it that the bladder cancer of mrs y was caused by passive smoking during her imprisonment given the fact that she was an ex-smoker herself? which liability percentage is reasonable in the given circumstance? what would be the life expectancy of mr x at the time of his death if the wrongful death not occurred? how long is mr x expected to survive, given his brain/ spinal cord injury, on a more probable than not basis? given the medical and non-medical evidence at hand regarding the circumstances of this traffic crash, what is the probability that mrs y was the driver? given the medical and non-medical evidence at hand regarding the circumstances of this car accident, what is the probability that mr x was wearing a seat belt? what is the probability that mrs y’s need for surgery resulted from the crash, vs. that it would have occurred at the same time if the crash had not happened?