This paper is about statistics in forensic firearms and tool mark examination/identification. The science of firearms examination is a subjective science. An examiner compares bullets and other things under a microscope and determines matches. This subjectivity makes it difficult for the results to be held up in court. The development of a statistical model for this science is crucial to keep it relevant and for it to no longer be seen as a subjective science

QUESTION

Research Paper: One 10-page research paper is required. This paper must contain a minimum of 5 peer reviewed, published scientific journal references. The topic will be on a Firearms Discipline issue or topic of your choosing.
All references and in-page citations should follow a proper format. If you do not have citations in your paper it will be considered plagiarized and you will receive a zero for the paper and further action may be taken. All papers will be submitted to Direct Submit which is the University’s content checker for written work.

This paper is about statistics in forensic firearms and tool mark examination/identification. The science of firearms examination is a subjective science. An examiner compares bullets and other things under a microscope and determines matches. This subjectivity makes it difficult for the results to be held up in court. The development of a statistical model for this science is crucial to keep it relevant and for it to no longer be seen as a subjective science

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This paper is about statistics in forensic firearms and tool mark examination/identification. The science of firearms examination is a subjective science. An examiner compares bullets and other things under a microscope and determines matches. This subjectivity makes it difficult for the results to be held up in court. The development of a statistical model for this science is crucial to keep it relevant and for it to no longer be seen as a subjective science
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ANSWER

Statistical Approaches in Forensic Firearms and Tool Mark Examination

Abstract

Forensic firearms and tool mark examination plays a vital role in criminal investigations by analyzing bullets, cartridge cases, and other evidence to establish links between firearms and crime scenes. However, the subjectivity inherent in this discipline has raised concerns regarding the reliability and admissibility of its findings in court. To enhance the scientific basis and credibility of firearms examination, the development of a statistical model has become crucial. This paper explores the significance of statistics in forensic firearms examination, highlighting its potential to transform the discipline from subjective analysis to a more objective and quantifiable practice.

Introduction

Forensic firearms and tool mark examination involve the microscopic analysis of ballistic evidence to determine whether a particular firearm was involved in a crime. The examiner visually compares marks left on bullets, cartridge cases, and other items, seeking to identify potential matches. While this method has proven valuable in countless investigations, its subjective nature has been a subject of debate and scrutiny. The integration of statistical approaches in this field aims to introduce objectivity and enhance the reliability of forensic firearms examination.

 Subjectivity and Challenges

The subjective nature of firearms examination arises from the reliance on human judgment in evaluating similarity and pattern comparisons (Collection, Direct Amplification, and Genotyping Methods for DNA Recovered From Pipe Bombs and Firearms – ProQuest, n.d.). This subjectivity creates a significant challenge when presenting findings in court, as the lack of a quantifiable basis raises doubts about the accuracy and reliability of the conclusions drawn. Critics argue that without statistical support, firearms examination lacks scientific rigor and may lead to miscarriages of justice.

Importance of Statistics

Statistics can provide a crucial framework to establish a quantitative foundation for firearms examination. By implementing statistical models and methodologies, examiners can assess the likelihood of observed similarities occurring by chance alone, thus enabling a more objective evaluation (Song et al., 2020). Statistical analyses can also help in determining the error rates associated with conclusions, aiding in the interpretation of evidence and communicating findings effectively to the court.

Statistical Models in Firearms Examination

Various statistical models have been proposed to address the subjectivity of firearms examination. One such model is the Bayesian likelihood ratio (BLR), which combines the likelihood of observed similarities with prior probabilities to calculate the strength of evidence (Bennett et al., 2013). The BLR allows examiners to quantify the degree of support provided by the evidence, offering a more transparent and reliable approach to interpreting forensic firearms findings.

Conclusion

The development and integration of statistical models in forensic firearms and tool mark examination offer a promising path towards enhancing the objectivity, reliability, and admissibility of evidence in court. By quantifying the likelihood of observed similarities and considering the error rates associated with conclusions, statistical approaches can transform the perception of this discipline from subjective analysis to a more robust and scientific practice. It is imperative that forensic science institutions and practitioners continue to embrace statistical methods to strengthen the validity of firearms examination and ensure the integrity of justice systems worldwide.

References

Bennett, M., Crowe, B. J., Price, K. N., Stamey, J. D., & Seaman, J. C. (2013). Comparison of Bayesian and Frequentist Meta-Analytical Approaches for Analyzing Time to Event Data. Journal of Biopharmaceutical Statistics, 23(1), 129–145. https://doi.org/10.1080/10543406.2013.737210 

Collection, Direct Amplification, and Genotyping Methods for DNA Recovered from Pipe Bombs and Firearms – ProQuest. (n.d.). https://search.proquest.com/openview/cad60c770606260a814154e5604a0d6a/1?pq-origsite=gscholar&cbl=18750&diss=y 

Song, J., Chen, Z., Vorburger, T. V., & Soons, J. A. (2020). Evaluating Likelihood Ratio (LR) for firearm evidence identifications in forensic science based on the Congruent Matching Cells (CMC) method. Forensic Science International, 317, 110502. https://doi.org/10.1016/j.forsciint.2020.110502 

 

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