Argument Analysis

O’Sullivan uses accumulated research literature to pose a claim that people are not good at identifying deception. He says that averagely, a normal person is accurate only 54% of the times that he is engaged where only 50% of that total is expected to be by chance. O’Sullivan and Ekman (2004) recently claimed that they have found 29 wizards of detecting lies. They say that they have peculiar focus and investment in this performance.

The two took tests to come to their conclusions where these tests involved ten people with five lying and five telling the truth, the test takers see each person on tape and then they try to identify who is lying and who is not. In the same breath, the tests involve three sections; the first is the area of opinion, then mock crime and finally emotion. These tests showed very low probabilities of any person attaining a wizard performance by chance, chance being when a test taker responds to the tapes as though they were flipping coins hence a 50/50 chance of being right in the 30 segment.

In this case, we assume that none of the 12,000 takers is a wizard so to determine this probability, we subscribe to two methods where on in the flip coin method and the other is the research based method. For the flip-coin method, it is considered irrelevant in this context because it was only used to formalize an assumption that O’Sullivan and Ekman made. However, the second method which is the research-based method is considered to be sober. It is more valid than the previous one and can give us better findings. It states that the data used is from previous results obtained by judges to estimate the accuracy on each section. If we consider that the students are more inaccurate than the professionals then the model would be conservative. The criteria was that the correct judgments were to be made in at least 9 tape segments, 8 crime videos and 8 emotion video sections however, evidence that is convincing has never been presented about lie detection wizardry.

In the second proposal, we see the involvement of Bond and Uysal. They claim that the two ignore one segment of experts that they have identified while they have misinterpreted the progression used to identify the others. Because of that Bond and his colleague say that Ekman have statistical flukes as is seen when they criticize the psychometric validity of the measures and the protocol that they used.

They claim that the use of binomial with empirical channels brought confusion to the normal distribution and to support this, they state that the expert lie detectors are not precise because it is only possible that they occurred by chance, secondly, the testing procedure used does not meet the classical psychometric test theory requirements. They show that a formula used to estimate the probability of getting at least 90% in a test and at least 80% in one of two is misleading because by then, we had identified 29 expert lie detectors with 14 scoring the above assumed and secondly, the test was sequential. That means that the second test depended on the first.

Bond and Uysal propose that it was more sensible to apply the formula to the second group unlike the first. Though the two agree that the thought of lie detection is attainable, they say that the use of chance distribution to certify a chance theory is inappropriate and that using a normal curve would have been better. The model where the research came from was based on lie detection being an ability that could be measured, equal distribution of the ability like others and the final was a concluding assumption that very few people would be perfectly accurate.

In conclusion, it is difficult to quantify these traits in society but it does not refute the fact that individuals who are very skilled in knowing who is lying and who is not are present.

References

O’Sullivan, M.,&Ekman, P. (2004). The wizards of deception detection. In Granhag,

P.A.,&Str¨omwell, L. (Eds.),

The Detection of Deception in Forensic Contexts (pp. 269–286). Cambridge, UK:

Cambridge University Press.

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