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Research on eyewitness testimony looks at systematic variables or estimator variables. Estimator variables are characteristics of the witness, event, testimony, or testimony evaluators. Systematic variables are variables that are, or have the possibility of, being controlled by the criminal justice system.
In eyewitness identification, in criminal law, evidence is received from a witness "who has actually seen an event and can so testify in court". [1]The Innocence Project states that "Eyewitness misidentification is the single greatest cause of wrongful convictions nationwide, playing a role in more than 75% of convictions overturned through DNA testing."
Wells argued that the rate of misidentifications are influenced by several methodological biases in the methods used by law enforcement to secure the identifications The system-variable versus estimator-variable distinction that Wells introduced in 1978 has so thoroughly permeated the nomenclature of the eyewitness literature that the terms are ...
Even though there are various thoughts and ideas regarding photographic memory, some people do have exceptional memories, which will help improve the accuracy of eyewitness identification. The frequency of eidetic imagery is low in adults and shows greatest frequency in early child development .
Eyewitness memory is a person's episodic memory for a crime or other witnessed dramatic event. [1] Eyewitness testimony is often relied upon in the judicial system.It can also refer to an individual's memory for a face, where they are required to remember the face of their perpetrator, for example. [2]
According to a 2001 survey of experts on eyewitness testimony, 87% found the weapon focus effect to be sufficiently reliable to form the basis of expert testimony in criminal trials. [34] Regarding eyewitness testimony, another study by Shaw and Skolnick (1994) discovered that sex plays a role in eyewitness memory and recall of a crime.
Thus unlike non-Bayesian approach where parameters of interest are assumed to be deterministic, but unknown constants, the Bayesian estimator seeks to estimate a parameter that is itself a random variable. Furthermore, Bayesian estimation can also deal with situations where the sequence of observations are not necessarily independent.
A consistent estimator is an estimator whose sequence of estimates converge in probability to the quantity being estimated as the index (usually the sample size) grows without bound. In other words, increasing the sample size increases the probability of the estimator being close to the population parameter.