Because so much emphasis in medicine is placed on test results to define disease, it is worth looking in more detail at the concepts that this view emphasizes. Essentially, this view implies that there is a “normal” state, and that deviations from this are therefore “abnormal”. The statistical models underpinning these ideas are probabilistic, that is, they rely on the understanding that when a test value exceeds a given cut-off level, then there is an increased probability of disease being present. Notice this is only a probability. It is also the case that if a given test result is high or low, that it might just be an extreme value, with no deeper significance. The probability that a given value is high by chance alone is usually expressed in terms of the symbol “p”. This means that the more extreme a value is, the less likely it is to have occurred by chance and the greater, therefore, is the probability that something is “wrong” with the body. When a high test result is found, this is taken to mean that disease is present. But this may not be the case. As there is considerable variation between people, the individual’s test result is compared against a “pooled” population standard. The more different you are, the more abnormal you are, and the more likely you are to be considered as diseased, or deviant in some other way.