The Organization of Scientific Area Committees for Forensic Science (OSAC) is trying to develop definitions of common technical terms that can be used across most forensic-science subject areas. "Bias" is one of these ubiquitous terms, but its statistical meaning does not conform to the usual dictionary definitions, such as "an inclination of temperament or outlook, especially: a personal and sometimes unreasoned judgment" \1/ or "the action of supporting or opposing a particular person or thing in an unfair way, because of allowing personal opinions to influence your judgment." \2/
I thought the following definition might be useful for forensic-science practitioners:
A systematic tendency for estimates or measurements to be above or below their true values. A study is said to be biased if its design is such that it systematically favors certain outcomes. An estimator of a population parameter is biased when the average value of the estimates (from an infinite number of samples) would not equal the value of the parameter. Bias arises from systematic as opposed to random error in the collection of units to be measured, the measurement of the units, or the process for estimating quantities based on the measurements.
It ties together some of the simplest definitions I have seen in textbooks and reference works on statistics -- namely:
Yadolah Dodge, The Concise Encyclopedia of Statistics 41 (2008): From a statistical point of view, the bias is defined as the difference between the expected value of a statistic and the true value of the corresponding parameter. Therefore, the bias is a measure of the systematic error of an estimator. If we calculate the mean of a large number of unbiased estimations, we will find the correct value. The bias indicates the distance of the estimator from the true value of the parameter. Comment: This is the definition for mathematical statistics. B. S. Everitt & A. Skrondal, The Cambridge Dictionary of Statistics 45 (4th ed. 2010) (citing Altman, D.G. (1991) Practical Statistics for Medical Research, Chapman and Hall, London): In general terms, deviation of results or inferences from the truth, or processes leading to such deviation. More specifically, the extent to which the statistical method used in a study does not estimate the quantity thought to be estimated, or does not test the hypothesis to be tested. In estimation usually measured by the difference between the expected value of an estimator and the true value of the parameter. An estimator for which E(θ-hat) = θ is said to be unbiased. See also ascertainment bias, recall bias, selection bias and biased estimator. Comment: The general definition (first sentence) fails to differentiate between random and systematic deviations. The “more specific” definition in the next sentence is limited to the definition in mathematical statistics. David H. Kaye & David A. Freedman, Reference Guide on Statistics, in Reference Manual on Scientific Evidence 283 (Federal Judicial Center & Nat’l Research Council eds., 3d ed. 2011): Also called systematic error. A systematic tendency for an estimate to be too high or too low. An estimate is unbiased if the bias is zero. (Bias does not mean prejudice, partiality, or discriminatory intent.) See nonsampling error. Comment: This one is intended to convey the essential idea to judges. David H. Kaye, Frequentist Methods for Statistical Inference, in Handbook of Forensic Statistics 39, 44 (D. Banks, K. Kafadar, D. Kaye & M. Tackett eds. 2020): [A]n unbiased estimator t of [a parameter] θ will give estimates whose errors eventually should average out to zero. Error is simply the difference between the estimate and the true value. For an unbiased estimator, the expected value of the errors is E(t – θ) = 0. Comment: Yet another version of the definition of an unbiased estimator of a population or model parameter. JCGM, International Vocabulary of Metrology – Basic and General Concepts and Associated Terms (VIM) (3d ed. 2012): measurement bias, bias -- estimate of a systematic measurement error Comment: The VIM misdefines bias as an estimate of bias. David S. Moore & George P. McCabe, Introduction to the Practice of Statistics 232 (2d ed. 1993): Bias. The design of a study is biased if it systematically favors certain outcomes. In a causal study, bias can result from confounding. Or can it?
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