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Journal articles on the topic 'Forensic statistics'

Author: Grafiati

Published: 4 June 2021

Last updated: 28 July 2024

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1

Ristow, Peter G., and Maria E. D’Amato. "Forensic statistics analysis toolbox (FORSTAT): A streamlined workflow for forensic statistics." Forensic Science International: Genetics Supplement Series 6 (December 2017): e52-e54. http://dx.doi.org/10.1016/j.fsigss.2017.09.006.

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2

EGELAND, THORE, and PETTER F. MOSTAD. "Statistical Genetics and Genetical Statistics: a Forensic Perspective*." Scandinavian Journal of Statistics 29, no. 2 (June 2002): 297–307. http://dx.doi.org/10.1111/1467-9469.00284.

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3

Curran, James M. "Statistics in forensic science." Wiley Interdisciplinary Reviews: Computational Statistics 1, no. 2 (September 2009): 141–56. http://dx.doi.org/10.1002/wics.33.

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4

Stern, Hal S. "Statistical Issues in Forensic Science." Annual Review of Statistics and Its Application 4, no. 1 (March 7, 2017): 225–44. http://dx.doi.org/10.1146/annurev-statistics-041715-033554.

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5

Jovanović, Jelena. "Statistical Interpretation of DNA Evidence in the Judicial Practice of Montenegro." Kriminalističke teme, no. 1 (July 23, 2021): 21–36. http://dx.doi.org/10.51235/kt.2021.21.1.21.

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Implementation of scientific methods in establishing facts relevants for criminalistics and legal proceedings can no longer be imagined without involving forensic examination of biological traces through analysis of DNA molecules. Legal norms for reaching a verdict bases on DNA evidance must be clearly set, and the interpretation and presentation of DNA evidence in court must be improved. Also, judges, prosecutors, lawyers must be constantly acquainted with and educated on the basics of forensics and the use of scientific evidence, in order to properly understand the DNA result and make correct judgments. Missing out some statistic-related facts and probability theory have led to certain legal omissions in both recent and past experience. Our goal with this article is to emphasize the importance of probability theories and statistics in forensic examination, particularly in interpretation of DNA results. Judges and lawyers accept the evidence based on their sense of reasoning. A necessary part is their greater participation and understanding of forensic reports and with the way of interpreting the evidence itself. It is responsibility of lawyers, judges and prosecutors is to understand the statistical evidence as much as possible. Standards of admissibility in court must be legally determined, and statistics and probability theory should be explained in detail to judges so they can make valid judgments.

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6

Fienberg, Stephen E. "Editorial: Statistics and forensic science." Annals of Applied Statistics 1, no. 2 (December 2007): 285–86. http://dx.doi.org/10.1214/07-aoas140.

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7

Steele, Christopher D., and David J. Balding. "Statistical Evaluation of Forensic DNA Profile Evidence." Annual Review of Statistics and Its Application 1, no. 1 (January 3, 2014): 361–84. http://dx.doi.org/10.1146/annurev-statistics-022513-115602.

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8

Crysup, Benjamin, August E. Woerner, Jonathan L. King, and Bruce Budowle. "Graph Algorithms for Mixture Interpretation." Genes 12, no. 2 (January 27, 2021): 185. http://dx.doi.org/10.3390/genes12020185.

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The scale of genetic methods are presently being expanded: forensic genetic assays previously were limited to tens of loci, but now technologies allow for a transition to forensic genomic approaches that assess thousands to millions of loci. However, there are subtle distinctions between genetic assays and their genomic counterparts (especially in the context of forensics). For instance, forensic genetic approaches tend to describe a locus as a haplotype, be it a microhaplotype or a short tandem repeat with its accompanying flanking information. In contrast, genomic assays tend to provide not haplotypes but sequence variants or differences, variants which in turn describe how the alleles apparently differ from the reference sequence. By the given construction, mitochondrial genetic assays can be thought of as genomic as they often describe genetic differences in a similar way. The mitochondrial genetics literature makes clear that sequence differences, unlike the haplotypes they encode, are not comparable to each other. Different alignment algorithms and different variant calling conventions may cause the same haplotype to be encoded in multiple ways. This ambiguity can affect evidence and reference profile comparisons as well as how “match” statistics are computed. In this study, a graph algorithm is described (and implemented in the MMDIT (Mitochondrial Mixture Database and Interpretation Tool) R package) that permits the assessment of forensic match statistics on mitochondrial DNA mixtures in a way that is invariant to both the variant calling conventions followed and the alignment parameters considered. The algorithm described, given a few modest constraints, can be used to compute the “random man not excluded” statistic or the likelihood ratio. The performance of the approach is assessed in in silico mitochondrial DNA mixtures.

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9

Fahmy, Hesham M. "Introduction to Statistics for Forensic Scientists." Technometrics 49, no. 3 (August 2007): 362. http://dx.doi.org/10.1198/tech.2007.s508.

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10

Weir, Bruce. "Introduction to Statistics for Forensic Scientists." American Statistician 61, no. 3 (August 2007): 274. http://dx.doi.org/10.1198/000313007x223711.

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11

Mortera, Julia. "DNA Mixtures in Forensic Investigations: The Statistical State of the Art." Annual Review of Statistics and Its Application 7, no. 1 (March 9, 2020): 111–42. http://dx.doi.org/10.1146/annurev-statistics-031219-041306.

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Forensic science has experienced a period of rapid change because of the tremendous evolution in DNA profiling. Problems of forensic identification from DNA evidence can become extremely challenging, both logically and computationally, in the presence of complicating features, such as in mixed DNA trace evidence. Additional complicating aspects are possible, such as missing data on individuals, heterogeneous populations, and kinship. In such cases, there is considerable uncertainty involved in determining whether or not the DNA of a given individual is actually present in the sample. We begin by giving a brief introduction to the genetic background needed for understanding forensic DNA mixtures, including the artifacts that commonly occur in the DNA amplification process. We then review different methods and software based on qualitative and quantitative information and give details on a quantitative method that uses Bayesian networks as a computational device for efficiently computing likelihoods. This method allows for the possibility of combining evidence from multiple samples to make inference about relationships from DNA mixtures and other more complex scenarios.

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12

Parai, Jacqueline L. "An Overview of Statistics for Forensic Pathologists." Academic Forensic Pathology 1, no. 3 (November 2011): 249–53. http://dx.doi.org/10.23907/2011.036.

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13

Shiroyan, Artem G. "FORENSIC: ESSENCE, STATISTICS AND METHODS OF PREVENTION." EKONOMIKA I UPRAVLENIE: PROBLEMY, RESHENIYA 3, no. 11 (2021): 44–47. http://dx.doi.org/10.36871/ek.up.p.r.2021.11.03.007.

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The article explores the concept of "forensic" and substantiates the relevance of this service, which is relatively new for domestic economic entities. The statistics of the occurrence of fraudulent actions are given and the methods of their prevention are substantiated. And also formulated a conclusion on the information presented in this article.

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14

Aitken, C. G. G. "The use of statistics in forensic science." Journal of the Forensic Science Society 27, no. 2 (March 1987): 113–15. http://dx.doi.org/10.1016/s0015-7368(87)72717-0.

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15

Gaudette, B. D. "The use of statistics in forensic science." Journal of the Forensic Science Society 27, no. 2 (March 1987): 117–18. http://dx.doi.org/10.1016/s0015-7368(87)72718-2.

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16

Zvárová, Jana. "Statistics and biomedical informatics in forensic sciences." Environmetrics 20, no. 6 (September 2009): 743–50. http://dx.doi.org/10.1002/env.975.

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17

Neumann, Cedric. "Defence against the modern arts: the curse of statistics: Part I—FRStat." Law, Probability and Risk 19, no. 1 (March 1, 2020): 1–20. http://dx.doi.org/10.1093/lpr/mgaa004.

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Abstract For several decades, legal and scientific scholars have argued that conclusions from forensic examinations should be supported by statistical data and reported within a probabilistic framework. Multiple models have been proposed to quantify and express the probative value of forensic evidence. Unfortunately, the use of statistics to perform inferences in forensic science adds a layer of complexity that most forensic scientists, court offices and lay individuals are not armed to handle. Many applications of statistics to forensic science rely on ad hoc strategies and are not scientifically sound. The opacity of the technical jargon that is used to describe these probabilistic models and their results, and the complexity of the techniques involved make it very difficult for the untrained user to separate the wheat from the chaff. This series of article is intended to help forensic scientists and lawyers recognize limitations and issues in tools proposed to interpret the results of forensic examinations. This article focuses on the tool proposed by the Latent Print Branch of the U.S. Defense Forensic Science Center (DFSC) and called FRStat. In this article, I explore the compatibility of the results outputted by FRStat with the language used by the DFCS to report the conclusions of their fingerprint examinations, as well as the appropriateness of the statistical modelling underpinning the tool and the validation of its performance.

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18

Kanchan, Tanuj, and Kewal Krishan. "Significance of descriptive statistics in forensic anthropology research." Journal of Forensic and Legal Medicine 20, no. 8 (November 2013): 1151. http://dx.doi.org/10.1016/j.jflm.2013.09.001.

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19

Neumann, Cedric, and Madeline Ausdemore. "Defence against the modern arts: the curse of statistics—Part II: ‘Score-based likelihood ratios’." Law, Probability and Risk 19, no. 1 (March 1, 2020): 21–42. http://dx.doi.org/10.1093/lpr/mgaa006.

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Abstract For several decades, legal and scientific scholars have argued that conclusions from forensic examinations should be supported by statistical data and reported within a probabilistic framework. Multiple models have been proposed to quantify and express the probative value of forensic evidence. Unfortunately, the use of statistics to perform inferences in forensic science adds a layer of complexity that most forensic scientists, court officers and lay individuals are not armed to handle. Many applications of statistics to forensic science rely on ad-hoc strategies and are not scientifically sound. The opacity of the technical jargon used to describe probabilistic models and their results, and the complexity of the techniques involved make it very difficult for the untrained user to separate the wheat from the chaff. This series of papers is intended to help forensic scientists and lawyers recognize limitations and issues in tools proposed to interpret the results of forensic examinations. This article focuses on tools that have been proposed to leverage the use of similarity scores to assess the probative value of forensic findings. We call this family of tools ‘score-based likelihood ratios’. In this article, we present the fundamental concepts on which these tools are built, we describe some specific members of this family of tools, and we compare them explore to the Bayes factor through an intuitive geometrical approach and through simulations. Finally, we discuss their validation and their potential usefulness as a decision-making tool in forensic science.

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20

Sivaloganathan, S. "Computers in Forensic Medicine—II." Medicine, Science and the Law 27, no. 4 (October 1987): 275–79. http://dx.doi.org/10.1177/002580248702700407.

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This is the second of two papers on the use of computers in a department of forensic medicine. It deals with the use of mainframe and desktop computers. The fields of application covered are bibliography, research, statistics, administration, inventories, accounts and word-processing.

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21

Ziegel, Eric R., and C. G. G. Aitken. "Statistics and the Evaluation of Evidence for Forensic Scientists." Technometrics 38, no. 1 (February 1996): 90. http://dx.doi.org/10.2307/1268932.

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22

MW and C. G. G. Aitken. "Statistics and the Evaluation of Evidence for Forensic Scientists." Journal of the American Statistical Association 91, no. 434 (June 1996): 915. http://dx.doi.org/10.2307/2291700.

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23

Konigsberg, Lyle W. "Review of: Essential Mathematics and Statistics for Forensic Science." Journal of Forensic Sciences 56, no. 6 (November 2011): 1682. http://dx.doi.org/10.1111/j.1556-4029.2011.01925.x.

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24

Jones, Albyn. "Special Section on Forensic Statistics Grows from JSM Session." CHANCE 19, no. 2 (March 2006): 5–6. http://dx.doi.org/10.1080/09332480.2006.10722778.

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25

Bécue-Bertaut, Mónica, Belchin Kostov, Annie Morin, and Guilhem Naro. "Rhetorical Strategy in Forensic Speeches: Multidimensional Statistics-Based Methodology." Journal of Classification 31, no. 1 (March 29, 2014): 85–106. http://dx.doi.org/10.1007/s00357-014-9148-9.

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26

Aitken, C. G. G. "The Eighth International Conference on Forensic Inference and Statistics." Law, Probability and Risk 12, no. 1 (February 18, 2013): 1–2. http://dx.doi.org/10.1093/lpr/mgt001.

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27

Szeremeta, Michal, Karolina Pietrowska, Anna Niemcunowicz-Janica, Adam Kretowski, and Michal Ciborowski. "Applications of Metabolomics in Forensic Toxicology and Forensic Medicine." International Journal of Molecular Sciences 22, no. 6 (March 16, 2021): 3010. http://dx.doi.org/10.3390/ijms22063010.

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Forensic toxicology and forensic medicine are unique among all other medical fields because of their essential legal impact, especially in civil and criminal cases. New high-throughput technologies, borrowed from chemistry and physics, have proven that metabolomics, the youngest of the “omics sciences”, could be one of the most powerful tools for monitoring changes in forensic disciplines. Metabolomics is a particular method that allows for the measurement of metabolic changes in a multicellular system using two different approaches: targeted and untargeted. Targeted studies are focused on a known number of defined metabolites. Untargeted metabolomics aims to capture all metabolites present in a sample. Different statistical approaches (e.g., uni- or multivariate statistics, machine learning) can be applied to extract useful and important information in both cases. This review aims to describe the role of metabolomics in forensic toxicology and in forensic medicine.

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28

Dr. Amughoro Onome Austin (Ph.D) and Akpotor, Vincent Afure. "THE EFFECT OF FORENSIC ACCOUNTING ON BANK PERFORMANCE." Finance & Accounting Research Journal 5, no. 11 (November 27, 2023): 333–41. http://dx.doi.org/10.51594/farj.v5i11.611.

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This study investigated the effects of forensic accounting on bank performance in Nigeria using four (4) measures of forensic accounting (money laundering fraud control, point of sales, money transfer fraud and automated teller machine fraud control) and one (1) performance measure (return on asset). Data were obtained from the annual reports and accounts of the banks and Central Bank of Nigeria Statistical Bulletin from 2012-2020. Data obtained were analyzed using descriptive statistics (mean, standard deviation, skewness, kurtosis and correlation) and inferential statistics (variance inflation factor and ordinary least square). Findings of the study showed that all the forensic accounting measures of the study (money laundering fraud, point of sales, money transfer fraud and automated teller machine fraud controls) negatively and significantly affected performance of banks in Nigeria. Based on the findings, it was recommended among others that bank management should employ forensic accounting by amending the existing statutes in such a way that forensic accountants are included in the management process. Through this, forensic accountants will have more tools to effectively deal with challenges in fraud control. Also, the negative relationship between forensic accounting and performance of banks suggests that it has not been effectively used by management; thus, there is need for Nigerian banks to further enhance and employ forensic accounting support services, fraud investigation and forensic audit by ensuring that they achieve what they intend to do for banks Keywords: Forensic Accounting, Bank, Performance, Nigeria.

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29

Anderson, L. M. "Statistics with Confidence. Confidence Intervals and Statistical Guidelines." Journal of Clinical Pathology 42, no. 12 (December 1, 1989): 1315. http://dx.doi.org/10.1136/jcp.42.12.1315-c.

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30

Evett, Ian. "Career story: Consultant Forensic Statistician." Significance 2, no. 1 (February 24, 2005): 34–37. http://dx.doi.org/10.1111/j.1740-9713.2005.00083.x.

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31

Tarran, Brian. "The limits of forensic evidence." Significance 16, no. 2 (April 2019): 12–15. http://dx.doi.org/10.1111/j.1740-9713.2019.01248.x.

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32

Carriquiry, Alicia, Heike Hofmann, Xiao Hui Tai, and Susan VanderPlas. "Machine learning in forensic applications." Significance 16, no. 2 (April 2019): 29–35. http://dx.doi.org/10.1111/j.1740-9713.2019.01252.x.

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33

Stanley Udogadi Nwawuba, Ben Friday Ukim, Andrew Ikhayere Imiefoh, Sunday Maliki Momoh, and Edeaghe Ehikhamenor. "Assessment of public awareness and willingness for establishment/storage of DNA profile in a national DNA database in Nigeria." World Journal of Advanced Research and Reviews 14, no. 2 (May 30, 2022): 204–11. http://dx.doi.org/10.30574/wjarr.2022.14.2.0441.

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One prominent aspect of forensic deoxyribonucleic acid testing is the establishment and expansion of centralized national forensic DNA databases and body of evidence have continued to emerge, demonstrating the extensive efficiency and effectiveness of the DNA database in assisting criminal investigation globally. Therefore, the present study aimed to examine public awareness on Forensic DNA Database and the willingness for storage of DNA profiles. The design used in this study is the survey research design and the sample size of this study was a total number of five hundred University of Benin students drawn from our population for empirical investigation. The study adopted descriptive statistics which involves the use of frequency and percentage. The result of this study revealed that majority of the respondent demonstrated an adequate level of awareness and knowledge (71.2%), showed good knowledge on the function of a national forensic DNA database (54.4%), demonstrated increased level of awareness and knowledge on the benefit of a national forensic DNA database (44.8%), and revealed that 422 (84.4%) were willing for the storage of their profiles in the national forensic DNA database.The study also observed that a vast proportion of the respondents indicated that fear of violation of individual’s privacy was the only barrier they considered for the storage of DNA profiles in the National Database and our observations provides a good a basis for reviewing and implementing policies that find a reasonable balance based on the creation of moral and ethical spectrum involving professionals in the area of forensics, law enforcement and the public, in particular, social groups which are less involved in genetics.

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34

Curran, James M. "The Statistical Interpretation of Forensic Glass Evidence." International Statistical Review 71, no. 3 (January 15, 2007): 497–520. http://dx.doi.org/10.1111/j.1751-5823.2003.tb00208.x.

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35

Walakumbura and Dharmarathna. "IMPACT OF FORENSIC ACCOUNTING KNOWLEDGE ON FRAUD DETECTION: WITH SPECIAL REFERENCE TO SRI LANKAN CONTEXT." Journal of Accountancy & Finance 9, no. 1 (August 20, 2022): 1–21. http://dx.doi.org/10.57075/jaf912201.

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There is a tendency in the growth of financial frauds across the world and to uncover those frauds, main concideration has been drawn towards the forensic accounting concepts. Consequenttly, that has been lead to conduct a study to examine the impact between forensic accounting knowledge on fraud detection.The researcher has adoped the dedcuctive approach for this study and the primary data has been collected from 165 accounting practitioners in Sri lanka. Fraud detection has been considered as the dependent variable while accounting skills, legal background, knowledge of procedures, and forensic accounting knowledge have been considered the independent variables. For the analysis purpose, descriptive statistics, factor analysis and inferential statistics such as multiple linear regression have been adopted. The findings of the study revealed that there is a significant impact in between the accounting skills, knowledge of procedures and knowledge of forensic accounting on fraud detection while there is no any significant impact between the legal background and the fraud detection.

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36

Jeong, Su Jin, Hyo Jung Lee, and Jae Won Lee. "Statistical considerations in forensic science: a review." Journal of the Korean Statistical Society 50, no. 3 (May 7, 2021): 703–29. http://dx.doi.org/10.1007/s42952-021-00123-7.

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37

Shanmugam, Ramalingam. "Two good books on forensic applications of statistics are reviewed." Journal of Statistical Computation and Simulation 81, no. 5 (May 2011): 659–60. http://dx.doi.org/10.1080/00949650802289096.

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38

Curran, James M. "Is forensic science the last bastion of resistance against statistics?" Science & Justice 53, no. 3 (September 2013): 251–52. http://dx.doi.org/10.1016/j.scijus.2013.07.001.

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39

Barbería, Eneko, Alexandre Xifró, and Josep Arimany-Manso. "Beneficial impact of forensic sources to cause-of-deaths statistics." Spanish Journal of Legal Medicine 43, no. 1 (January 2017): 1–4. http://dx.doi.org/10.1016/j.remle.2017.01.001.

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40

Fuglsby, Cami, Christopher P. Saunders, and JoAnn Buscaglia. "U-statistics for estimating performance metrics in forensic handwriting analysis." Journal of Statistical Computation and Simulation 90, no. 6 (February 24, 2020): 1082–117. http://dx.doi.org/10.1080/00949655.2020.1715406.

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41

Wang, Xu, He-Juan Jiao, Hai-Biao Zhu, and He Yuan. "Development of China′s forensic science in statistics: 2005-2016." Journal of Forensic Science and Medicine 4, no. 1 (2018): 59. http://dx.doi.org/10.4103/jfsm.jfsm_80_17.

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42

Gupta, Abhinav, and Divya Singhal. "Global median filtering forensic method based on Pearson parameter statistics." IET Image Processing 13, no. 12 (October 17, 2019): 2045–57. http://dx.doi.org/10.1049/iet-ipr.2018.6074.

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43

Crawford, Amy, Anyesha Ray, and Alicia Carriquiry. "A database of handwriting samples for applications in forensic statistics." Data in Brief 28 (February 2020): 105059. http://dx.doi.org/10.1016/j.dib.2019.105059.

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44

Chen, Yung-Fou, and Paul Kuei-chi Tseng. "The Boundary of Artificial Intelligence in Forensic Science." DIALOGO 10, no. 1 (November 30, 2023): 83–90. http://dx.doi.org/10.51917/dialogo.2023.10.1.5.

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Artificial intelligence (AI) is poised to revolutionize forensic science by enhancing evidence analysis and aiding in crime-solving. This paper explores AI applications in forensic fields, focusing on DNA mixture analysis, the cause of death diagnosis, and the challenges AI faces in forensic science. It addresses the limitations of AI in forensic science, emphasizing the necessity for transparent AI systems used ethically. Forensic errors are often not due to misclassification by forensic scientists but stem from incompetence, fraud, weak scientific foundations, or organizational deficiencies, resulting in wrongful convictions. Systemic issues like dependence on presumptive tests and inadequate defense contribute to errors. Inconsistencies plague various domains of forensic science, including fingerprint and DNA analysis. Establishing standardized analytical procedures can enhance consistency and credibility in forensic science. AI applications in forensic anthropology, pathology, genetics, and statistics are explored, with a focus on their potential and challenges.

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45

Makushkina, Oxana, and Svetlana Polubinskaya. "Prevention of Dangerous Acts of Persons with Mental Disorders: Organization, Statistics, Trends." Russian Journal of Criminology 13, no. 5 (October 31, 2019): 791–802. http://dx.doi.org/10.17150/2500-4255.2019.13(5).791-802.

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Dangerous and violent acts of mentally disordered persons that pose a threat to the health and lives of people around them frequently attract the media attention and become a subject of public debates. Prevention of such actions is one of the most important tasks of the psychiatric care system. This article describes the organization of psychiatric prevention at federal and regional levels, involving all medical organizations that provide outpatient and inpatient psychiatric care. The primary focus is on forensic psychiatric prevention of dangerous behavior of the mentally ill. One of the forms of preventive measures is active dispensary observation of patients prone to dangerous acts due to their mental condition. This observation is carried out by medical organizations together with the internal affairs’ agencies. However, the key issue of forensic psychiatric prevention is the execution of the compulsory medical measures imposed by the court and provided for by the Criminal Code of the Russian Federation. Besides, the measures aimed at preventing repeat crimes also include psychiatric care of convicts as a separate branch of forensic psychiatric prevention. The authors present the data of ten-year monitoring of medical services that make it possible to assess the scope, main directions and effectiveness of prevention. This investigation uses the data from the state statistical reports for the Russian Federation and the subjects of the Russian Federation from 2008 to 2017. On this basis, the authors identify the trends in prevention of the dangerous acts of mentally disordered offenders in the psychiatric care system and formulate the main directions for the improvement. In this connection, the authors pay special attention to the use of standardized risk assessment tools to predict dangerous behavior of the mentally ill as one of the ways to increase the effectiveness of psychiatric prevention. Such tools are widely used in foreign forensic psychiatric practice, as well as in criminal justice institutions. The authors describe principal methodological approaches to the development of such tools in foreign and domestic research. According to the authors, the use of such techniques for the risk assessment of dangerous behavior in mentally disordered persons and the development of individual medical and rehabilitation plans on their basis would expand the possibilities for forensic psychiatric prevention.

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46

Garvin, Heather M., Rachel Dunn, Sabrina B. Sholts, M. Schuyler Litten, Merna Mohamed, Nathan Kuttickat, and Noah Skantz. "Forensic Tools for Species Identification of Skeletal Remains: Metrics, Statistics, and OsteoID." Biology 11, no. 1 (December 25, 2021): 25. http://dx.doi.org/10.3390/biology11010025.

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Although nonhuman remains constitute a significant portion of forensic anthropological casework, the potential use of bone metrics to assess the human origin and to classify species of skeletal remains has not been thoroughly investigated. This study aimed to assess the utility of quantitative methods in distinguishing human from nonhuman remains and present additional resources for species identification. Over 50,000 measurements were compiled from humans and 27 nonhuman (mostly North American) species. Decision trees developed from the long bone data can differentiate human from nonhuman remains with over 90% accuracy (>98% accuracy for the human sample), even if all long bones are pooled. Stepwise discriminant function results were slightly lower (>87.4% overall accuracy). The quantitative models can be used to support visual identifications or preliminarily assess forensic significance at scenes. For species classification, bone-specific discriminant functions returned accuracies between 77.7% and 89.1%, but classification results varied highly across species. From the study data, we developed a web tool, OsteoID, for users who can input measurements and be shown photographs of potential bones/species to aid in visual identification. OsteoID also includes supplementary images (e.g., 3D scans), creating an additional resource for forensic anthropologists and others involved in skeletal species identification and comparative osteology.

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47

Dawid, A. P., and J. Mortera. "Coherent Analysis of Forensic Identification Evidence." Journal of the Royal Statistical Society: Series B (Methodological) 58, no. 2 (July 1996): 425–43. http://dx.doi.org/10.1111/j.2517-6161.1996.tb02091.x.

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48

Yang, Sheng-Hsiung, Jermiah Y. Shen, Matt S. Chang, and Gaston J. Wu. "Differentiation of vehicle top coating paints using pyrolysis-gas chromatography/mass spectrometry and multivariate chemometrics with statistical comparisons." Analytical Methods 7, no. 4 (2015): 1527–34. http://dx.doi.org/10.1039/c4ay02730b.

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49

Aitken, Colin, Franco Taroni, and Silvia Bozza. "The Role of the Bayes Factor in the Evaluation of Evidence." Annual Review of Statistics and Its Application 11, no. 1 (April 22, 2024): 203–26. http://dx.doi.org/10.1146/annurev-statistics-040522-101020.

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The use of the Bayes factor as a metric for the assessment of the probative value of forensic scientific evidence is largely supported by recommended standards in different disciplines. The application of Bayesian networks enables the consideration of problems of increasing complexity. The lack of a widespread consensus concerning key aspects of evidence evaluation and interpretation, such as the adequacy of a probabilistic framework for handling uncertainty or the method by which conclusions regarding how the strength of the evidence should be reported to a court, has meant the role of the Bayes factor in the administration of criminal justice has come under increasing challenge in recent years. We review the many advantages the Bayes factor has as an approach to the evaluation and interpretation of evidence.

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Omorogbe Owen, Stephen, Kelvin Orhue Osazee, Edeaghe Ehikhamenor, and Udogadi Nwawuba Stanley. "Awareness level on the relevance of forensics in criminal investigation in Nigeria." Journal of Forensic Science and Research 5, no. 1 (October 15, 2021): 053–57. http://dx.doi.org/10.29328/journal.jfsr.1001028.

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The main aim of forensic science is to gather intelligence to enable the judge to credible and logical decisions in the court by means of scientific approach through evaluation of evidence for the administration of justice, and country around the world now considers forensic methodology as the gold standard for criminal investigation. Therefore, the present study examined the level of awareness on the relevance of forensics in criminal investigation in Nigeria. The design used in this study is the survey research design and the sample size of this study was a total of one hundred personnel of law enforcement and the judiciary. The study adopted descriptive statistics which involves the use of frequency and percentage. The result of the present study revealed that the participants were distributed socio-demographically as follows; there was an observable higher number of male participants (68%) relative to the female participants (32%), As per age distribution, a larger population of the participants were found to be > 40 years of age with 55%, and it was observed that age between 35-39 years ranked the least with 15%. On educational level, the result of the present study revealed that majority of the participants possesses a bachelor’s degree as the highest level of educational qualification with 75% from a pool of 100% of participants. The present study further examined responses on the relevance of forensics in criminal investigation, and the result revealed an inadequate level of awareness on the relevance of forensics in criminal investigation. Therefore, the study recommends that the Nigerian Police Force and the Judiciary should collaborate with Universities running programs on forensics for trainings.

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