Objective: The risk of mistaken identification for innocent suspects in lineups can be estimated by correcting the overall error rate by the number of people in the lineup. We compared this nominal size correction to a new effective size correction, which adjusts the error rate for the number of plausible lineup members.

Hypotheses: We hypothesized that (a) increasing lineup bias would increase misidentifications of a designated innocent suspect; (b) with the effective size correction, increasing lineup bias would also increase the estimate of innocent-suspect misidentifications; and (c) with the nominal size correction, lineup bias would have no effect on the estimate of innocent-suspect misidentifications.

Method: In a reanalysis of previous literature, we obtained 10 data sets from Open Science Framework. In three new experiments ( = 686, 405, and 1,531, respectively), participants observed a staged crime and completed a fair or biased lineup.

Results: In the reanalysis of previous literature, less than four of six lineup members were identified frequently enough to be classified as plausible, = 3.78, 95% confidence interval [CI: 2.20, 5.36]. In the new experiments, increasing lineup bias increased mistaken identifications of a designated innocent suspect, odds ratio () = 5.50, 95% CI [2.77, 10.95] and also increased the effective size-corrected estimate of innocent-suspect misidentifications, = 3.04, 95% CI [2.13, 4.33]. With the nominal size correction, lineup bias had no effect on the estimate of innocent-suspect misidentifications, = 0.84, 95% CI [0.60, 1.18].

Conclusions: Most lineups include a combination of plausible and implausible lineup members. Contrary to the nominal size correction, which ignores implausible lineup members, the effective size correction is sensitive to implausible lineup members and accounts for lineup bias when estimating the risk of innocent suspect misidentifications. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

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http://dx.doi.org/10.1037/lhb0000538DOI Listing

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