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

21.2K
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...
21.2K
Probability in Statistics01:14

Probability in Statistics

22.4K
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...
22.4K
Introduction to Statistics01:17

Introduction to Statistics

62.8K
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...
62.8K
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

15.4K
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...
15.4K
Identifying Statistically Significant Differences: The F-Test01:14

Identifying Statistically Significant Differences: The F-Test

3.3K
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...
3.3K
Introduction to Nonparametric Statistics01:28

Introduction to Nonparametric Statistics

1.3K
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...
1.3K

You might also read

Related Articles

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

Sort by
Same author

Ensuring Fairness in Detecting Mild Cognitive Impairment with MRI.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2025
Same author

Enhancing clinical outcome predictions through effective sample size evaluation in graph-based digital twin modeling.

BioData mining·2025
Same author

Perceptual and technical barriers in sharing and formatting metadata accompanying omics studies.

Cell genomics·2025
Same author

Erratum: A latent transfer learning method for estimating hospital-specific post-acute healthcare demands following SARS-CoV-2 infection.

Patterns (New York, N.Y.)·2025
Same author

AI as an accelerator for defining new problems that transcends boundaries.

BioData mining·2025
Same author

Preoperative anemia is an unsuspecting driver of machine learning prediction of adverse outcomes after lumbar spinal fusion.

The spine journal : official journal of the North American Spine Society·2025
Same journal

TPOT-NN: augmenting tree-based automated machine learning with neural network estimators.

Genetic programming and evolvable machines·2025
Same journal

Editorial Introduction.

Genetic programming and evolvable machines·2022
Same journal

Editorial introduction.

Genetic programming and evolvable machines·2022
Same journal

Software review: Pony GE2.

Genetic programming and evolvable machines·2021
Same journal

Highlights of genetic programming 2020 events.

Genetic programming and evolvable machines·2021
Same journal

Editorial introduction.

Genetic programming and evolvable machines·2021
See all related articles

Related Experiment Video

Updated: Jan 24, 2026

Facilitating Drug Discovery: An Automated High-content Inflammation Assay in Zebrafish
07:50

Facilitating Drug Discovery: An Automated High-content Inflammation Assay in Zebrafish

Published on: July 16, 2012

14.8K

Automated discovery of test statistics using genetic programming.

Jason H Moore1, Randal S Olson1, Yong Chen1

  • 1Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia PA 19104, USA.

Genetic Programming and Evolvable Machines
|May 21, 2019
PubMed
Summary
This summary is machine-generated.

Automating the creation of new statistical test methods accelerates scientific discovery. A genetic programming approach successfully generated novel test statistics comparable in power to the established t-test.

Keywords:
Genetic ProgrammingOptimizationStatisticsT-Test

More Related Videos

Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction
16:23

Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction

Published on: February 26, 2014

14.9K
GENPLAT: an Automated Platform for Biomass Enzyme Discovery and Cocktail Optimization
11:38

GENPLAT: an Automated Platform for Biomass Enzyme Discovery and Cocktail Optimization

Published on: October 24, 2011

15.9K

Related Experiment Videos

Last Updated: Jan 24, 2026

Facilitating Drug Discovery: An Automated High-content Inflammation Assay in Zebrafish
07:50

Facilitating Drug Discovery: An Automated High-content Inflammation Assay in Zebrafish

Published on: July 16, 2012

14.8K
Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction
16:23

Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction

Published on: February 26, 2014

14.9K
GENPLAT: an Automated Platform for Biomass Enzyme Discovery and Cocktail Optimization
11:38

GENPLAT: an Automated Platform for Biomass Enzyme Discovery and Cocktail Optimization

Published on: October 24, 2011

15.9K

Area of Science:

  • Statistics
  • Computer Science
  • Machine Learning

Background:

  • Developing new statistical test statistics is a manual and time-consuming process.
  • Existing methods require extensive theoretical knowledge and evaluation of mathematical functions.
  • Automation of test statistic discovery is lacking but would significantly speed up research.

Purpose of the Study:

  • To develop an automated system for discovering new statistical test statistics.
  • To overcome the challenges in automating this process, including encoding desirable properties and exploring candidate solutions.

Main Methods:

  • Utilized genetic programming, a machine learning technique, to automate the discovery of test statistics.
  • The system was designed to develop and explore candidate mathematical solutions representing test statistics.
  • Incorporated knowledge of desirable properties for effective test statistics into the discovery method.

Main Results:

  • The genetic programming system successfully discovered new test statistics.
  • The discovered test statistics demonstrated power comparable to the well-known t-test.
  • This was achieved for the specific case of comparing sample means from two distributions with equal variances.

Conclusions:

  • Automated discovery of test statistics using genetic programming is feasible.
  • This approach can accelerate the development of novel statistical tools.
  • The system shows potential for discovering powerful alternatives to existing statistical tests.