Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance, comparing...
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
Bonferroni Test01:10

Bonferroni Test

The Bonferroni test is a statistical test named after Carlo Emilio Bonferroni, an Italian mathematician best known for Bonferroni inequalities. This statistical test is a type of multiple comparison test to determine which means are different than the rest. Bonferroni test can minimize the Type 1 error by reducing the significance level alpha, which otherwise increases with sample pairs.
The means of different samples are first paired in all possible combinations.
The null hypothesis of the...
Eyewitness Memory01:22

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.
One such error is memory distortion, which occurs because human memory does not function like a...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

What is the best way to present likelihood ratios? A review of past research and recommendations for future research.

Science & justice : journal of the Forensic Science Society·2025
Same author

Does explaining the meaning of likelihood ratios improve lay understanding?

Science & justice : journal of the Forensic Science Society·2025
Same author

Incorrect statistical reasoning in Guyll et al. leads to biased claims about strength of forensic evidence.

Proceedings of the National Academy of Sciences of the United States of America·2024
Same author

How signature complexity affects expert and lay ability to distinguish genuine, disguised and simulated signatures.

Journal of forensic sciences·2024
Same author

Author's response.

Journal of forensic sciences·2024
Same author

Author's response.

Journal of forensic sciences·2023

Related Experiment Video

Updated: Jul 18, 2026

Preparation and Application of a New Bacterial Biosensor for the Presumptive Detection of Gunshot Residue
07:09

Preparation and Application of a New Bacterial Biosensor for the Presumptive Detection of Gunshot Residue

Published on: May 9, 2019

Statistical inference and forensic evidence: evaluating a bullet lead match.

Suzanne O Kaasa1, Tiamoyo Peterson, Erin K Morris

  • 1Department of Psychology and Social Behavior, University of California, Irvine, California, USA.

Law and Human Behavior
|December 23, 2006
PubMed
Summary

Mock jurors correctly interpreted highly diagnostic forensic evidence but struggled with non-diagnostic evidence. Confidence in statistical ability was key to appropriate interpretation of forensic evidence.

More Related Videos

Enhanced Genetic Analysis of Single Human Bioparticles Recovered by Simplified Micromanipulation from Forensic ‘Touch DNA’ Evidence
11:49

Enhanced Genetic Analysis of Single Human Bioparticles Recovered by Simplified Micromanipulation from Forensic ‘Touch DNA’ Evidence

Published on: March 9, 2015

Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm
09:49

Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm

Published on: December 24, 2015

Related Experiment Videos

Last Updated: Jul 18, 2026

Preparation and Application of a New Bacterial Biosensor for the Presumptive Detection of Gunshot Residue
07:09

Preparation and Application of a New Bacterial Biosensor for the Presumptive Detection of Gunshot Residue

Published on: May 9, 2019

Enhanced Genetic Analysis of Single Human Bioparticles Recovered by Simplified Micromanipulation from Forensic ‘Touch DNA’ Evidence
11:49

Enhanced Genetic Analysis of Single Human Bioparticles Recovered by Simplified Micromanipulation from Forensic ‘Touch DNA’ Evidence

Published on: March 9, 2015

Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm
09:49

Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm

Published on: December 24, 2015

Area of Science:

  • Forensic Science
  • Legal Psychology
  • Cognitive Psychology

Background:

  • Mock jurors evaluate forensic evidence in legal settings.
  • Understanding how jurors interpret statistical data from forensic evidence is crucial for legal proceedings.

Purpose of the Study:

  • To assess undergraduate mock jurors' ability to interpret statistical data regarding the diagnostic value of forensic evidence.
  • To determine factors influencing jurors' appropriate use of statistical information in a simulated trial.

Main Methods:

  • 295 undergraduate mock jurors were presented with a homicide trial summary.
  • Key evidence included a bullet lead match with varying diagnostic values (highly diagnostic, non-diagnostic, unknown) or no forensic evidence.

Main Results:

  • Jurors, as a group, appropriately weighted highly diagnostic evidence over non-diagnostic evidence.
  • This accurate interpretation was primarily driven by jurors confident in their statistical abilities.
  • Jurors lacking statistical confidence failed to differentiate evidence types and disregarded forensic evidence.

Conclusions:

  • Mock juror interpretation of forensic evidence is influenced by statistical confidence.
  • Legal professionals should consider juror statistical literacy when presenting forensic evidence.
  • Further research is needed on improving juror comprehension of statistical forensic data.