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

Weighted Mean00:57

Weighted Mean

6.0K
While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
6.0K
Skewness01:06

Skewness

16.0K
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...
16.0K
Types of Skewness01:09

Types of Skewness

15.9K
If the frequency distribution of a data set is more inclined towards smaller or larger values, the distribution is said to be skewed. If data values are skewed to the right, then the distribution is called positively skewed. Conversely, if the plot is skewed to the left, the distribution is called negatively skewed.
For instance, in the middle of a pandemic, the geographical distribution of vaccine coverage may be positively skewed towards populations in the global north countries. However,...
15.9K
Regression Toward the Mean01:52

Regression Toward the Mean

6.7K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.7K
Modified Boxplots00:57

Modified Boxplots

10.7K
A standard box and whisker plot informs us about the spread of the data in a given sample. One can identify the minimum value, maximum value, first quartile value, second quartile or median value, and third quartile.
However, the box plot does not tell the reader about outliers - values that lie far from the center of the data. We can modify the standard box and whisker plot to identify the outliers and visualize the actual spread of the data in a sample.
Initially, we calculate the adjusted...
10.7K
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

1.1K
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
1.1K

You might also read

Related Articles

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

Sort by
Same author

LIVES VERSUS LIVELIHOODS: THE IMPACT OF THE GREAT RECESSION ON MORTALITY AND WELFARE.

The quarterly journal of economics·2026
Same author

Clinician behavior when skin tone affects test results.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Academic detailing is effective at altering clinician practice and patient outcomes for opioid use disorder.

Addiction (Abingdon, England)·2026
Same author

Publisher Correction: Reproducibility and robustness of economics and political science research.

Nature·2026
Same author

Reproducibility and robustness of economics and political science research.

Nature·2026
Same author

CellPheno: A High-throughput Computational Platform for Quantifying Cellular Resolution Whole Brain Microscopy Images.

bioRxiv : the preprint server for biology·2026
Same journal

MesoSplats: Texture Synthesis with Gaussian Splatting.

IEEE transactions on visualization and computer graphics·2026
Same journal

GLLA: A Unified Force-Directed Graph Layout Framework Supporting Local Adjustments.

IEEE transactions on visualization and computer graphics·2026
Same journal

Multi-Perception Crowd: Learning to combine entity and implicit perception for diverse crowd simulation.

IEEE transactions on visualization and computer graphics·2026
Same journal

Hiding in Plain Sight: Camouflaging Real-world Objects.

IEEE transactions on visualization and computer graphics·2026
Same journal

RTF2Mesh: Restricted Tangent Face Based Mesh Compression With Neural Displacement Fields.

IEEE transactions on visualization and computer graphics·2026
Same journal

Practical Occluder Generation for Mobile Games.

IEEE transactions on visualization and computer graphics·2026
See all related articles

Related Experiment Video

Updated: Dec 5, 2025

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
09:33

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

28.9K

Selection-Bias-Corrected Visualization via Dynamic Reweighting.

David Borland, Jonathan Zhang, Smiti Kaul

    IEEE Transactions on Visualization and Computer Graphics
    |October 20, 2020
    PubMed
    Summary
    This summary is machine-generated.

    Dynamic reweighting (DR) is a new computational method to mitigate selection bias in visual data analysis. It helps create accurate, bias-corrected visualizations for complex datasets, improving decision-making validity.

    More Related Videos

    Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
    06:55

    Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

    Published on: January 8, 2020

    14.9K
    Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
    07:05

    Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

    Published on: October 27, 2016

    9.5K

    Related Experiment Videos

    Last Updated: Dec 5, 2025

    Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
    09:33

    Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

    Published on: July 28, 2013

    28.9K
    Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
    06:55

    Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

    Published on: January 8, 2020

    14.9K
    Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
    07:05

    Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

    Published on: October 27, 2016

    9.5K

    Area of Science:

    • Data Visualization
    • Information Visualization
    • Computational Statistics

    Background:

    • Visual analysis of large-scale data from complex systems is prevalent across industries.
    • Retrospective visual analysis, especially with high-dimensional data, is susceptible to selection bias.
    • Dynamic filtering and grouping operations exacerbate selection bias risks, threatening insight validity.

    Purpose of the Study:

    • To introduce Dynamic Reweighting (DR) as a novel computational approach for selection bias mitigation in visual analysis.
    • To present the DR workflow, key visualization designs, and supporting statistical methods.
    • To address the limitations of existing bias transparency methods by offering bias mitigation.

    Main Methods:

    • Development of the Dynamic Reweighting (DR) computational workflow.
    • Design of specific visualization techniques to support the DR process.
    • Integration of statistical methods to underpin DR-driven bias correction.

    Main Results:

    • Demonstration of the DR workflow for crafting bias-corrected visualizations.
    • Presentation of use cases, particularly from the medical domain, illustrating DR's applicability.
    • Incorporation of findings from domain expert interviews to validate the approach.

    Conclusions:

    • Dynamic Reweighting (DR) offers a computational solution for mitigating selection bias in visual data analysis.
    • The DR approach enhances the validity and generalizability of insights derived from complex datasets.
    • This method empowers users to create bias-corrected visualizations, improving data-driven decision-making.