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

Skewness01:06

Skewness

12.2K
The measures of central tendency calculated from a data set may not reveal much about its intrinsic distribution. If a plot is made of the data set’s values, the mean and the median may not only differ, but also the plot may have more values on one side of the central tendencies. Such a data set is said to be skewed towards that side.
The longer the tail of the plot on one side, the more skewed it is. The skewness of a data set’s values suggests that the measures of central tendency...
12.2K
Pareto Chart00:52

Pareto Chart

6.9K
A Pareto chart is a bar graph or a combination of both line and bar graphs. The bar lengths represent the individual values or the frequency, while the lines represent the cumulative total values. In this chart, the longest bars are arranged on the left and the shortest bars on the right, which makes it easier to read and interpret the data. It can also be called a Pareto diagram or Pareto analysis.
The Pareto chart is named after the Italian economist Vilfredo Pareto, who described the Pareto...
6.9K
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

267
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
267
Central Tendency: Analysis01:10

Central Tendency: Analysis

179
Measures of central tendency are tools used in biostatistics to identify the average or center of a dataset. They offer a single representative value for understanding and summarizing data distribution.
The mean is one such measure, calculated by totaling all values in a dataset and dividing by the number of values. For instance, the mean blood pressure reading (120, 130, 140, 150) would be 135. However, the mean can be affected by extreme values or outliers.
The median, another measure,...
179
Wilcoxon Signed-Ranks Test for Median of Single Population01:14

Wilcoxon Signed-Ranks Test for Median of Single Population

196
The Wilcoxon signed-rank test for the median of a single population is a nonparametric test used to evaluate whether the median of a population differs from a specified value. Unlike parametric tests, it does not require data to follow a normal distribution, making it suitable for non-normal or small samples. The test begins by calculating the difference (d) between each observation and the hypothesized median. The absolute values of these differences are ranked in ascending order, with ties...
196
Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

2.6K
The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an...
2.6K

You might also read

Related Articles

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

Sort by
Same author

The Future Strikes Back: Using Future Treatments to Detect and Reduce Hidden Bias.

Sociological methods & research·2022
Same journal

Rental Arrears and Perceived Risk of Eviction among U.S. Renter Households by Household Composition, Race, and Ethnicity 2020 to 2024.

Socius : sociological research for a dynamic world·2026
Same journal

Prospective Attitude about the Importance of Planning Pregnancies Is Associated with Retrospective Attitude toward a Specific Pregnancy.

Socius : sociological research for a dynamic world·2026
Same journal

High School Employment and Intergenerational Mobility in Education: A Causal Decomposition Approach in a Period of Widespread Teenage Work.

Socius : sociological research for a dynamic world·2026
Same journal

Tag-Team Parenting: Trends in Work Schedule Synchronization among Families with Young Children.

Socius : sociological research for a dynamic world·2026
Same journal

Changes in Americans' Views on Who Should Provide and Pay for Assistance to Older Adults with Activity Limitations 2012 to 2022.

Socius : sociological research for a dynamic world·2026
Same journal

Medically Assisted Reproduction in the United States: A Focus on Parents 40 and Older.

Socius : sociological research for a dynamic world·2026
See all related articles

Related Experiment Video

Updated: Aug 8, 2025

Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI
06:09

Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI

Published on: July 21, 2023

1.3K

The U.S. Wealth Distribution: Off the Charts.

Fabian T Pfeffer1, Asher Dvir-Djerassi1

  • 1University of Michigan, Ann Arbor, MI, USA.

Socius : Sociological Research for a Dynamic World
|March 6, 2023
PubMed
Summary
This summary is machine-generated.

Understanding U.S. wealth inequality is complex. This study offers an interactive visualization of the 2019 wealth distribution, integrating data from indebted households to multibillionaires for a comprehensive view.

Keywords:
debtinequalityskewwealth

More Related Videos

Author Spotlight: A Non-Invasive Tool to Assess and Differentiate Fat Patterns in Liver Using 3D Dixon MRI
05:37

Author Spotlight: A Non-Invasive Tool to Assess and Differentiate Fat Patterns in Liver Using 3D Dixon MRI

Published on: October 20, 2023

1.5K
Analysis of SEC-SAXS data via EFA deconvolution and Scatter
10:59

Analysis of SEC-SAXS data via EFA deconvolution and Scatter

Published on: January 28, 2021

9.1K

Related Experiment Videos

Last Updated: Aug 8, 2025

Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI
06:09

Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI

Published on: July 21, 2023

1.3K
Author Spotlight: A Non-Invasive Tool to Assess and Differentiate Fat Patterns in Liver Using 3D Dixon MRI
05:37

Author Spotlight: A Non-Invasive Tool to Assess and Differentiate Fat Patterns in Liver Using 3D Dixon MRI

Published on: October 20, 2023

1.5K
Analysis of SEC-SAXS data via EFA deconvolution and Scatter
10:59

Analysis of SEC-SAXS data via EFA deconvolution and Scatter

Published on: January 28, 2021

9.1K

Area of Science:

  • Economics
  • Sociology
  • Data Visualization

Background:

  • U.S. wealth inequality is rising, attracting significant public and scientific attention.
  • Existing research often focuses on wealth concentration at the top (e.g., top 1%) or inequality within the remaining population (99%).
  • These separate approaches limit a holistic understanding of the overall wealth distribution.

Purpose of the Study:

  • To address the challenge of visualizing both extreme top-end wealth concentration and broader distributional inequality.
  • To provide an intuitive and interactive tool for exploring the full spectrum of U.S. wealth distribution in 2019.
  • To bridge the gap between different perspectives on wealth inequality measurement.

Main Methods:

  • Development of an interactive visualization tool.
  • Integration of data spanning the entire U.S. population, from indebted households to multibillionaires.
  • Focus on the year 2019 for wealth distribution analysis.

Main Results:

  • The visualization effectively integrates data on wealth concentration at the top and inequality across the broader population.
  • It allows users to intuitively grasp the scale and shape of the 2019 U.S. wealth distribution.
  • The tool facilitates a more comprehensive understanding of how different segments of the population relate within the overall wealth landscape.

Conclusions:

  • Jointly visualizing different facets of wealth inequality is crucial for a complete understanding.
  • Interactive tools can enhance public and scientific intuition regarding complex economic distributions.
  • This approach offers a novel method for studying and communicating wealth inequality in the United States.