Over the decennaries. there has been much contention on the effectivity of clinical anticipations which are largely based on experts’ intuition. Researchs from the past decennaries have proven that statistical methods are more accurate than clinical anticipations and other researches examined heuristic rules used in foretelling and judging results during times when there is uncertainness or deficient information. Although trusting upon these heuristics simplifies judgement to a certain grade. this may take to terrible mistakes. Basically. there are three heuristic rules proposed by Kahneman and Tversky ( 1974 ) .
The first is called the handiness heuristic. wherein anticipations are made based on the information available. The 2nd is grounding. wherein anticipations are based on a series of numerical estimations or “anchors” . The 3rd one is called the representativeness heuristic. wherein anticipations are made based on the subsistence of seemingly similar instances. This paper surveies one of these heuristic rules viz. . representativeness heuristic. to demo how this heuristic can take to bias on clinical anticipations and hence. demo that such heuristics are. so. less accurate than anticipations based upon statistical methods.
First. the writer feels compelled to give a small background on a few surveies over the ongoing clinical-statistical contention. In 1996. Grove and Meehl proved that statistical method “is about constantly equal to or superior to clinical method” ( p. 293 ) in footings of truth in anticipation. They analyzed secondary informations coming from 136 published English researches since the 1920s which dealt with the anticipation of health-related phenomena or human behavior.
These researches should besides incorporate at least one of each anticipation — that is. at least one clinical anticipation or one based on human judgement and at least one mechanical or statistical anticipation. As have mentioned earlier. all of the researches they included in their studied proved that statistical method is so about ever equal to or superior to clinical method because statistical anticipation obtained from organized informations are about ever free from prejudice. These informations are observed from existent experiences and are recorded with precise instruments alternatively of trusting on unaided memory.
Furthermore. statistical illations are more nonsubjective than the human head which can be bias at times or which can pretermit certain of import properties that are necessary before even reasoning on the consequence and therefore. sometimes ensuing to terrible mistakes in anticipations. Hence. anticipations obtained from these statistical methods produce indifferent consequences in contrast with anticipations made from human judgement. There are many grounds and illustrations that can demo the high quality of statistical method over clinical method.
In this paper. one type of heuristic is presented based on the observations of Kahneman and Tversky in their paper On the Psychology of Prediction ( 1973 ) . Their paper is chosen due to the fact that it presents how people. specifically clinicians. justice certain events based on similar events that happened in the yesteryear. In the terminal. this paper shows how such a heuristic ( representativeness ) can take to certain and perchance terrible mistakes in judgement as compared to the event of utilizing statistical method.
Data analysis. treatment and decision are all based upon the findings of Kahneman and Tversky ( 1973 ) and Grove and Meehl ( 1996 ) . In 1973. Kahneman and Tversky discussed two categories of anticipation. the categorical anticipation. in which anticipations are presented nominally and numerical anticipation. in which anticipations are presented in numerically. They foremost examined category anticipations by spliting 248 participants into three groups — 69 participants for the base-rate¬ group. 65 participants for the similarity group and 114 participants for the anticipation group.
The base-rate group was asked to think the per centum of freshman alumnus pupils in the US who are enrolled as of the clip the survey was in advancement in each of the nine Fieldss of specialisation viz. . Business Administration. Computer Science. Engineering. Humanistic disciplines and Education. Law. Library Science. Medicine. Physical and Life Sciences. and Social Science and Social Work. The similarity group was given a personality study ( see Kahneman and Tversky. p. 38 ) and asked to rank the nine countries in footings of “how similar is Tom W. to the typical alumnus pupil in each of the undermentioned nine Fieldss of alumnus specialisation? ” . The anticipation group. which consists of alumnus pupils in psychological science at three major universities in the United States was besides given the same personality study as that given to the similarity group with some extra information ( see Kahneman and Tversky. p. 239 ) and was asked to foretell Tom W’s pick of specialisation.
Kahneman and Tversky compared the consequences of these three groups by showing a tabular array ( see Kahneman and Tversky. p. 238 ) and calculating the product-moment correlativities between the columns of the tabular array. In so making. they confirmed their hypothesis that most people predict certain events based on representativeness. Kahneman and Tversky explained that this happens because all the participants ignored certain of import characteristics before pulling their decisions. In this manner. they violate the normative regulations of information.
The participants. fundamentally. ignored the three types of information relevant in any statistical analysis viz. . anterior or background information ( presented to the participants utilizing base rates of Fieldss of alumnus specialisation. specific grounds refering the single instance ( presented to the participants utilizing the personality study of Tom W. ) and the expected truth of anticipation. The statistically right method of foretelling Tom W’s pick of specialisation would be to compare the comparative weights assigned to specific grounds and anterior information with that of expected truth.
As Kahneman and Tversky explains “when expected truth lessenings. anticipations should go more regressive. that is. closer to the outlooks based on anterior information” ( p. 239 ) . However. the participants in their survey predicted without even sing the anterior chances assigned to the specific grounds as described in Tom W’s personality study. Kahneman and Tversky ( 1973 ) besides examined in their paper how numerical anticipations can besides take to bias judgements or terrible mistakes. In a survey designed analogously with their survey on categorical anticipations. they showed that people besides tend to foretell by representativeness.
That is. most predict an result utilizing a mark that is most representative of the description they were provided. Kahneman and Tversky’s showed us that whether people were given nominal or numerical informations. they tend to foretell results by representativeness. Most may believe that foretelling by representativeness is more efficient than statistical methods since one should merely see similar or representative events while statistical methods require strict ( as most think ) undertakings such as detecting and garnering informations and calculating for excessively many steps such as mean. standard divergence and the similar.
However. this can go less accurate since they fail to see some of import parts in their analysis before pulling decisions whereas statistical methods consider all of the of import parts required earlier wholly analysing a information. Such statistical and mechanical methods cut down prejudices since these methods rely on precise mensurating instruments than heuristic methods which rely about wholly to memory or yesteryear cognition which are most of the clip insufficient or can non entirely stand for a certain event.
Furthermore. consequences derived from heuristic methods such as representativeness can change depending upon the perceptual experience of different people. Consequences from statistical method. on the other manus. vary merely because of fluctuation between groups or within-groups. But even if informations is given to five hundred different people. every bit long as the information is still the same. it will still give the same consequence.