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

Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

5.7K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
5.7K
Introduction to Normal Distributions01:29

Introduction to Normal Distributions

193
Standardized test scores often follow a symmetric distribution that can be modeled with the normal distribution, a fundamental concept in statistics. This distribution is particularly useful for interpreting test performance fairly across populations, as it provides a mathematical framework for understanding variability and central tendency in large datasets.From Histogram to Frequency DistributionRaw test data are often displayed using histograms, where the height of each bar represents the...
193
Normal Distribution01:11

Normal Distribution

18.3K
The normal, a continuous distribution, is the most important of all the distributions. Its graph is a bell-shaped symmetrical curve, which is observed in almost all disciplines. Some of these include psychology, business, economics, the sciences, nursing, and, of course, mathematics. Some instructors may use the normal distribution to help determine students’ grades. Most IQ scores are normally distributed. Often real-estate prices fit a normal distribution. The normal distribution is...
18.3K
Probability Distributions01:32

Probability Distributions

13.3K
 The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
13.3K
Central Limit Theorem01:14

Central Limit Theorem

21.5K
The central limit theorem, abbreviated as clt, is one of the most powerful and useful ideas in all of statistics. The central limit theorem for sample means says that if you repeatedly draw samples of a given size and calculate their means, and create a histogram of those means, then the resulting histogram will tend to have an approximate normal bell shape. In other words, as sample sizes increase, the distribution of means follows the normal distribution more closely.
The sample size, n, that...
21.5K
Percentile01:18

Percentile

9.7K
A percentile indicates the relative standing of a data value when data are sorted into numerical order from smallest to largest. It represents the percentages of data values that are less than or equal to the pth percentile. For example, 15% of data values are less than or equal to the 15th percentile.
9.7K

You might also read

Related Articles

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

Sort by
Same author

ERG1A K<sup>+</sup> channel increases intracellular calcium concentration through modulation of calsequestrin1 in C<sub>2</sub>C<sub>12</sub> myotubes.

Scientific reports·2025
Same author

Model-free measurement of case influence in structural equation modeling.

Frontiers in psychology·2024
Same author

Adolescents' Perception of the Threat of Sexual Harassment: The Development of an Index.

Journal of child sexual abuse·2019
Same author

Relationships Among Classical Test Theory and Item Response Theory Frameworks via Factor Analytic Models.

Educational and psychological measurement·2018
Same author

Repetitive negative thinking predicts depression and anxiety symptom improvement during brief cognitive behavioral therapy.

Behaviour research and therapy·2015
Same author

Simulating multivariate g-and-h distributions.

The British journal of mathematical and statistical psychology·2009
Same journal

Bayesian Machine Learning Tools for Alcohol Use Disorder Research: The bpaup R Package.

Multivariate behavioral research·2026
Same journal

A Unified Framework for Jointly modelling Response Times and Item Position Effects in Computer-Based Learning Assessments.

Multivariate behavioral research·2026
Same journal

Generalizability Theory Applied to Daily Relationship Quality: Substantive and Statistical Directions.

Multivariate behavioral research·2026
Same journal

A Modularized Higher-Order Diagnostic Classification Model for Clustered Attribute Hierarchies.

Multivariate behavioral research·2026
Same journal

Generalizing Causal Effects to a Target Population Without Individual-Level Data from the Target Population.

Multivariate behavioral research·2026
Same journal

betaselectr: Selective (and Proper) Standardization in Structural Equation Models.

Multivariate behavioral research·2026
See all related articles

Related Experiment Video

Updated: Mar 29, 2026

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.4K

Simulating Univariate and Multivariate Nonnormal Distributions through the Method of Percentiles.

Jennifer Koran1, Todd C Headrick1, Tzu Chun Kuo1

  • 1a Section on Statistics and Measurement, Southern Illinois University , Carbondale.

Multivariate Behavioral Research
|November 27, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a percentile-based power method for statistical modeling, offering a superior alternative to traditional moment-based estimators when data is limited. This approach simplifies calculations and enhances accuracy in simulations and distribution fitting.

More Related Videos

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.2K
Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
08:13

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

6.9K

Related Experiment Videos

Last Updated: Mar 29, 2026

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.4K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.2K
Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
08:13

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

6.9K

Area of Science:

  • Statistics
  • Computational Statistics
  • Data Science

Background:

  • Traditional statistical methods often rely on moment-based estimators (skewness, kurtosis) which may be unknown or difficult to compute.
  • Limited data availability, such as only having percentile information, poses challenges for conventional distribution fitting and simulation.

Purpose of the Study:

  • To develop a novel power method polynomial transformation utilizing percentiles for Monte Carlo simulations and distribution fitting.
  • To provide a method that functions effectively even when conventional moment estimators are unknown or data is unavailable.

Main Methods:

  • Derivation of a standard normal-based power method polynomial transformation using the method of percentiles.
  • Development of a procedure for simulating power method distributions with specified statistical properties (median, inter-decile range, skew, kurtosis, correlations).
  • Modification of the percentile power method for generating non-normal distributions with specified Pearson correlations.

Main Results:

  • The percentile-based power method provides closed-form solutions for polynomial coefficients, eliminating the need for numerical equation solving.
  • Monte Carlo simulations demonstrate that percentile-based estimators exhibit substantially lower relative bias compared to conventional product-moment estimators.
  • The method successfully generates non-normal distributions with specified Pearson correlations and is illustrated using educational assessment data.

Conclusions:

  • The percentile power method offers a robust and computationally efficient alternative for statistical modeling, particularly in scenarios with limited data or unknown moments.
  • This method enhances the accuracy and applicability of Monte Carlo simulations and distribution fitting.
  • The technique is broadly applicable, as demonstrated by its use with real-world educational assessment statistics.