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Related Concept Videos

Statistical Analysis: Overview01:11

Statistical Analysis: Overview

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When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
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Accuracy and Errors in Hypothesis Testing01:13

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Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
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Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
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Systematic Error: Methodological and Sampling Errors01:15

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In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
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A complete procedure to test a claim about population standard deviation or population variance is explained here.
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Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
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Standardized Method for Measuring Collection Efficiency from Wipe-sampling of Trace Explosives
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Quantifying the strength of firearms comparisons based on error rate studies.

Nada Aggadi1, Kimberley Zeller2, Tom Busey1

  • 1Department of Psychological and Brain Sciences, Indiana University-Bloomington, Bloomington, Indiana, USA.

Journal of Forensic Sciences
|October 30, 2024
PubMed
Summary

Forensic firearms examiners

Keywords:
calibrationerror ratesfirearm evidencelikelihood ratiosordered probit modelstrength of evidence

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Area of Science:

  • Forensic Science
  • Firearms Examination
  • Tool Mark Analysis

Background:

  • Forensic firearms and tool mark examiners compare bullets and cartridge cases to determine source.
  • Examiners use conclusion scales (e.g., Identification, Elimination) to communicate findings.
  • Current scales lack direct calibration to evidence strength, relying on indirect error rate studies.

Purpose of the Study:

  • To reanalyze firearms and cartridge case comparison data from error rate studies.
  • To generate quantitative measures of evidence strength for examiner comparisons.
  • To produce likelihood ratios for assessing the strength of forensic evidence.

Main Methods:

  • Utilized an ordered probit model to analyze examiner responses in error rate studies.
  • Aggregated data from firearms and cartridge case comparisons.
  • Calculated likelihood ratios to quantify the strength of evidence.

Main Results:

  • Likelihood ratios derived from the analysis can be as low as less than 10.
  • This quantitative measure contrasts with current qualitative scales.
  • The findings suggest current terminology may overstate evidence strength.

Conclusions:

  • The study provides a quantitative method for evaluating the strength of firearms evidence.
  • Current conclusion scales used by examiners may overstate the evidence strength by orders of magnitude.
  • There is a need to calibrate examiner language with empirical data for accurate communication.