Dispersion matters: Diagnostics and control data computer simulation in Concealed Information Test studies.

ABSTRACT: Binary classification has numerous applications. For one, lie detection methods typically aim to classify each tested person either as "liar" or as "truthteller" based on the given test results. To infer practical implications, as well as to compare different methods, it is essential to assess the diagnostic efficiency, such as demonstrating the number of correctly classified persons. However, this is not always straightforward. In Concealed Information Tests (CITs), the key predictor value (probe-irrelevant difference) for "truthtellers" is always similar (zero on average), and "liars" are always distinguished by a larger value (i.e., a larger number resulting from the CIT test, as compared to the zero baseline). Thereby, in general, the larger predictor values a given CIT method obtains

SUBMITTER: Lukacs G 

PROVIDER: S-EPMC7531802 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature