By Anders Kallen
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6 misclassifications. To obtain 201 is therefore rather unlikely! However, this is not really a statistical problem. The same problem occurs in medicine, for example with screening activities. Consider the situation where a physician is carrying out a routine health check-up on a patient. As a part of this he takes a ‘lab status’: he draws blood which he sends to a laboratory. In return he gets measurements of a number of chemicals in various blood compartments. In order to assess the clinical implications of these numbers, to understand their relation to health, the laboratory also provides reference ranges for each of the measurements.
These problems stem from the fact that in epidemiology there are substantially more issues with sampling, competing risks, confounders and bias than there are with purely experimental procedures, which can be set up to eliminate most of these problems. find the effect without the exposure, but not with the exposure, and also when there is a true increase in the probability of the effect after the intervention or exposure. In order to attribute the effect to the exposure we must understand how much the effect occurs at random.
5), or not. Your confidence in this statement can then be expressed in the p-value, which is a probability in the tradition of the frequentist definition above, namely the number of experiments with an unbiased coin that will produce the outcome you observed or ‘worse’. This p-value is used to express your confidence in the statement that the coin is unbiased. Similarly, you can express confidence in the actual parameter value π using confidence intervals, to be described in more detail in later chapters.