Related Experiment Video
Updated: Oct 19, 2025
An Experimental Analysis of Children's Ability to Provide a False Report about a Crime
Published on: May 3, 2016
A within-statement baseline comparison for detecting lies
Brianna L Verigin1,2, Ewout H Meijer1, Aldert Vrij2
1Forensic Psychology Section, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands.
Abstract:
To make veracity judgements in individual cases, practitioners may rely on baselining. That is, they may evaluate a statement relative to a baseline statement that is known to be truthful. We investigated whether a within-statement verbal baseline comparison could enhance discriminatory accuracy. Participants (n = 148) read an alibi statement of a mock suspect and provided a veracity judgement regarding a critical two-hour period within the alibi statement. This critical element was either deceptive or truthful and was embedded into an otherwise truthful story. Half of the participants received additional instructions to use the surrounding truthful elements of the statement as a baseline. Instructing participants to make a within-statement baseline comparison did not improve the accuracy of credibility assessments.
More Related Videos
Related Concept Videos
01:14Understanding Deception
01:13Multiple Comparison Tests
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
01:12Types of Errors: Detection and Minimization
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
01:00Detection of Gross Error: The Q Test
01:19Testing a Claim about Standard Deviation
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
01:16Null and Alternative Hypotheses
The null hypothesis, denoted by H0 is a statement of no difference between the variables—they are not related. This can often be considered the status quo. As a result if you cannot accept the null, it requires some action.
The alternative hypothesis, denoted by H1 or Ha, is a claim about the...