Interpreting Evidence

Robertson, Bernard; Vignaux, Georges; Berger, Charles E.H.

doi:10.1002/9781118492475

Cited by 66 publications

(38 citation statements)

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“…It is argued by some that interpretation is the quintessential distinguishing feature, or inherent characteristic, of forensic science (Robertson, Vignaux, & Berger, 2016c).…”

Section: The Interpretation Of Scientific Evidencementioning

confidence: 99%

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A review of the current quality standards framework supporting forensic science: Risks and opportunities

Doyle¹

2019

WIREs Forensic Science

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The publication and implementation of the latest editions of the International Organization for Standardization (ISO) 9001:2015 and ISO/International Electrochemical Commission (IEC) 17025:2017 together with the recent publication of the first book on quality management in forensic science provide an opportunity for this review. It details the changes to the structure, content and scope of the latest editions and assesses the impact on forensic science. Key terms are defined and explained. The review examines the effectiveness of the quality standards framework (QSF) supporting forensic science in the regions of Australia and New Zealand, Europe and North America. The concept of a hierarchy is introduced as a means of classifying standards to better navigate the crowded landscape. The main body of the review identifies risks to the QSF and opportunities for improvement. This article is categorized under: Forensic Science in Action/Crime Scene Investigation > Education and Formation Forensic Chemistry and Trace Evidence > Presentation and Evaluation of Forensic Science Output Jurisprudence and Regulatory Oversight > Expert Evidence and Narrative Forensic Biology > Interpretation of Biological Evidence

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“…It is argued by some that interpretation is the quintessential distinguishing feature, or inherent characteristic, of forensic science (Robertson, Vignaux, & Berger, 2016c).…”

Section: The Interpretation Of Scientific Evidencementioning

confidence: 99%

“…Publication of a second edition of Interpreting Evidence (Robertson et al, 2016c) and the ENFSI guide on evaluative reporting (ENFSI, 2015) should provide fresh impetus.…”

Section: The Interpretation Of Scientific Evidencementioning

confidence: 99%

A review of the current quality standards framework supporting forensic science: Risks and opportunities

Doyle¹

2019

WIREs Forensic Science

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“…The statistician would then have a well-defined cut-off point. However, this approach is similar to the effect associated with significance testing known as the 'falloff-the cliff' effect (Robertson et al, 2016). If the difference is just one side of the point, meaningful support is declared; if the difference is just the other side of the cut-off point, no meaningful support is declared.…”

Section: Discussionmentioning

confidence: 99%

Statistical significance—meaningful or not

Aitken

Wilson

Sleeman

2018

Law, Probability and Risk

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Statistical tests with large sample sizes can have large power. Power is the ability to detect an effect. Detection is indicated by a result which is statistically significant. A test with large power will detect a very small effect. This very small effect may not be meaningful in the context of the analysis being conducted. The courts have the perception that for an effect to be meaningful it is necessary for the effect to be statistically significant. However, statistical significance is not a sufficient condition for an effect to be meaningful. This can lead to a difficulty where testimony of no meaningful effect is interpreted by counsel as one of no statistically significant effect. Should the difference between a meaningful effect and a statistically significant effect be explained in reports and if so, how? Some possible answers are proposed.

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“…In forensic inference and statistics there is wide support for the position that the logically correct way for a forensic scientist to evaluate the strength of forensic evidence is using a likelihood ratio [1,2,3]. A likelihood ratio is the probability of the observed evidence if the prosecution hypothesis were true versus if the defense hypothesis were true [4,5]. Acoustic-phonetic approaches to forensic voice comparison predominantly used the multivariate kernel density (MVKD [6]) model to calculate likelihood ratios [7,8,9,10,11,12] (see also a review in [13]).…”

Section: Introductionmentioning

confidence: 99%

Likelihood Ratio Calculation in Acoustic-Phonetic Forensic Voice Comparison: Comparison of Three Statistical Modelling Approaches

Enzinger

2016

Interspeech 2016

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This study compares three statistical models used to calculate likelihood ratios in acoustic-phonetic forensic-voicecomparison systems: Multivariate kernel density, principal component analysis kernel density, and a multivariate normal model. The data were coefficient values obtained from discrete cosine transforms fitted to human-supervised formant-trajectory measurements of tokens of /iau/ from a database of recordings of 60 female speakers of Chinese. Tests were conducted using high-quality recordings as nominal suspect samples and mobileto-landline transmitted recordings as nominal offender samples. Performance was assessed before and after fusion with a baseline automatic mel frequency cepstral coefficient Gaussian mixture model universal background model system. In addition, Monte Carlo simulations were used to compare the output of the statistical models to true likelihood-ratio values calculated on the basis of the distribution specified for a simulated population.

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Interpreting Evidence

Cited by 66 publications

References 0 publications

A review of the current quality standards framework supporting forensic science: Risks and opportunities

A review of the current quality standards framework supporting forensic science: Risks and opportunities

Statistical significance—meaningful or not

Likelihood Ratio Calculation in Acoustic-Phonetic Forensic Voice Comparison: Comparison of Three Statistical Modelling Approaches

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