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

Statistical Significance01:50

Statistical Significance

22.0K
Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this...
22.0K
Probability in Statistics01:14

Probability in Statistics

23.5K
Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts.
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...
23.5K
Introduction to Statistics01:17

Introduction to Statistics

64.1K
The science of statistics involves collecting, analyzing, interpreting, and presenting data. The method of collecting, organizing, and summarizing data is called descriptive statistics. The systematic method of drawing inferences from the sample data and predicting unknown characteristics of a population is called inferential statistics.
In statistics, the collection of individuals or objects under study is called population. The idea of sampling is to select a portion of the larger population...
64.1K
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

16.6K
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.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
16.6K
Identifying Statistically Significant Differences: The F-Test01:14

Identifying Statistically Significant Differences: The F-Test

3.8K
The F-test is used to compare two sample variances to each other or compare the sample variance to the population variance. It is used to decide whether an indeterminate error can explain the difference in their values. The underlying assumptions that allow the use of the F-test include the data set or sets are normally distributed, and the data sets are independent of each other. The test statistic F is calculated by dividing one variance by another. In other words, the square of one standard...
3.8K
Introduction to Nonparametric Statistics01:28

Introduction to Nonparametric Statistics

1.4K
Nonparametric statistics offer a powerful alternative to traditional parametric methods, useful when assumptions about the population distribution cannot be made. Unlike parametric tests, which require data to follow a specific distribution with well-defined parameters (such as the mean and standard deviation), nonparametric tests do not require such constraints. This makes them particularly valuable when dealing with small sample sizes, skewed data, or ordinal and categorical variables.
One of...
1.4K

You might also read

Related Articles

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

Sort by
Same author

GWAS SVatalog: a visualization tool to aid fine-mapping of GWAS loci with structural variations.

Heredity·2025
Same author

On the analysis of genetic association with long-read sequencing data.

PLoS genetics·2025
Same author

Large-scale genome-wide association analyses identify novel genetic loci and mechanisms in hypertrophic cardiomyopathy.

Nature genetics·2025
Same author

Directional integration and pathway enrichment analysis for multi-omics data.

Nature communications·2024
Same author

SLCO5A1 and synaptic assembly genes contribute to impulsivity in juvenile myoclonic epilepsy.

NPJ genomic medicine·2023
Same author

HostSeq: a Canadian whole genome sequencing and clinical data resource.

BMC genomic data·2023

Related Experiment Video

Updated: Feb 6, 2026

A Cognitive Paradigm to Investigate Interference in Working Memory by Distractions and Interruptions
10:38

A Cognitive Paradigm to Investigate Interference in Working Memory by Distractions and Interruptions

Published on: July 16, 2015

14.1K

The evidential statistical paradigm in genetics.

Lisa J Strug1

  • 1Program in Genetics and Genome Biology, The Hospital for Sick Children, The Centre for Applied Genomics, The Hospital for Sick Children, Division of Biostatistics and Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada.

Genetic Epidemiology
|August 19, 2018
PubMed
Summary

The evidential statistical paradigm (EP) offers a robust alternative to P-values for measuring statistical evidence in genetics research. This approach provides a clearer interpretation of results, especially with large datasets and complex analyses.

Keywords:
foundations of statisticsinferencelikelihood paradigmmultiple hypothesis testingstatistical evidence

More Related Videos

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

6.1K
The HoneyComb Paradigm for Research on Collective Human Behavior
06:48

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

9.8K

Related Experiment Videos

Last Updated: Feb 6, 2026

A Cognitive Paradigm to Investigate Interference in Working Memory by Distractions and Interruptions
10:38

A Cognitive Paradigm to Investigate Interference in Working Memory by Distractions and Interruptions

Published on: July 16, 2015

14.1K
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

6.1K
The HoneyComb Paradigm for Research on Collective Human Behavior
06:48

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

9.8K

Area of Science:

  • Genetics
  • Biostatistics
  • Statistical Evidence

Background:

  • Reproducibility concerns have intensified the debate on P-values as statistical evidence measures.
  • The American Statistical Association warns against P-value misuse, highlighting the need for alternatives.
  • Interpreting P-values requires consideration of sample size and experimental design, especially in genetics.

Purpose of the Study:

  • Introduce and review the evidential statistical paradigm (EP) as an alternative to Bayesian and Frequentist approaches.
  • Demonstrate the application of EP in human genetics, specifically Cystic Fibrosis genetic association studies.
  • Provide a framework for measuring statistical evidence with EP, addressing covariates, model misspecification, and composite hypotheses.

Main Methods:

  • Discuss theoretical developments in the evidential statistical paradigm.
  • Apply EP to Cystic Fibrosis genetic association analyses.
  • Present novel graphical displays and highlight computational software for EP.

Main Results:

  • EP measures statistical evidence effectively, even with covariates and model misspecification.
  • The EP framework justifies the necessity of replication in genetic association studies.
  • Novel graphical displays and software facilitate the computation and interpretation of EP measures.

Conclusions:

  • The evidential statistical paradigm offers a more scientifically consistent approach to statistical evidence than traditional P-values.
  • EP is particularly relevant for analyzing large and complex genetic datasets.
  • This paradigm supports the critical requirement for replication in genetic association studies.