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Related Concept Videos

Inductive Reasoning00:59

Inductive Reasoning

Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. It is uncertain and operates in degrees to which the conclusions are credible. As such, inductive arguments can be weak or strong, rather than valid or invalid, and conclusions can be used to formulate testable, falsifiable hypotheses.Inductive reasoning is common in descriptive science. A life scientist makes observations and records them. This data can be qualitative or...
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Eyewitness Memory

Eyewitness memory refers to the recollection of events by someone who has directly witnessed them, often serving as critical evidence in legal settings. This type of memory is commonly used in criminal cases where a witness describes details like a suspect's appearance, clothing, or behavior during a crime. However, despite its perceived reliability, eyewitness memory is prone to significant errors.
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While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?
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Inductively Coupled Plasma-Mass Spectrometry (ICP-MS): Interferences

Inductively coupled plasma–mass spectrometry (ICP–MS) is a highly selective and sensitive technique for accurate elemental analysis. Though the analysis of ICP–MS mass spectra is comparatively straightforward, it is affected by spectroscopic and non-spectroscopic interferences. Spectroscopic interferences arise when the plasma contains ionic species with an m/z value the same as the analyte ion. Spectroscopic interference can be categorized as isobaric, polyatomic ions, and refractory oxide ion...
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Two items of evidence, no putative source: an inference problem in forensic intelligence.

Franco Taroni1, Silvia Bozza, Alex Biedermann

  • 1School of Criminal Justice, The University of Lausanne, 1015 Lausanne-Dorigny, Switzerland. Franco.Taroni@unil.ch

Journal of Forensic Sciences
|January 4, 2007
PubMed
Summary

This study addresses challenges in linking cases using scientific evidence. It proposes a framework using probability and graphical models to improve inference for common source and case linkage, aiding intelligence analysts.

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Area of Science:

  • Forensic Science
  • Intelligence Analysis
  • Decision Theory

Background:

  • Intelligence analysts often link cases based on similarities in scientific evidence.
  • Evaluating evidence similarities and relevance presents inferential challenges.
  • Current methods lack a structured approach to probabilistic reasoning in case linkage.

Purpose of the Study:

  • To analyze inferential difficulties in associating cases using scientific evidence.
  • To propose a framework distinguishing between inference to common source and inference to case linkage.
  • To utilize probability theory and graphical models for enhanced analytical insights.

Main Methods:

  • Decomposition of reasoning into inference to common source and inference to case linkage.
  • Application of probability theory to model inferential processes.
  • Introduction of graphical models to visualize dependencies between propositions.
  • Incorporation of decision theory principles for practical application.

Main Results:

  • A structured approach to evaluating evidence similarities without a known source.
  • A method for assessing the relevance of evidence in case linkage.
  • Probabilistic models clarifying the nature of inferences in forensic casework.
  • Graphical representations illustrating independence and dependence relationships.

Conclusions:

  • The proposed framework enhances the rigor of scientific evidence-based case linkage.
  • Probability and decision theory offer valuable tools for intelligence analysts.
  • Improved inference processes can lead to more informed decisions on case association.