Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Histogram

Histogram

The histogram is a graphical representation in the x-y form of data distribution in a data set. The horizontal x-axis is labeled with what the data represents (for instance, distance from your home to school). The vertical y-axis is labeled either frequency or relative frequency (or percent frequency or probability).
A histogram graph consists of contiguous (adjoining) boxes. The heights of the bars correspond to frequency values. The graph will have the same shape with respective labels. The...

Probability Histograms

Probability Histograms

A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.

Relative Frequency Histogram

Relative Frequency Histogram

The relative frequency depicts the proportion of data points that have each value. The frequency tells the number of data points that have each value. Like the histogram, a relative frequency histogram also has the same shape with a horizontal scale (the x-axis), but the vertical scale (the y-axis) is marked with relative frequencies (percentages of the whole) instead of actual frequencies. A relative frequency histogram is a graphical representation of a frequency distribution where the...

Review and Preview

Review and Preview

Data are individual items of information obtained from a population or sample. Data may be classified as qualitative (categorical), quantitative continuous, or quantitative discrete. Because it is not practical to measure the entire population in a study, researchers use samples to represent the population. A random sample is a representative group from the population chosen by using a method that gives each individual in the population an equal chance of being included in the sample. Random...

Review and Preview

Review and Preview

In statistics, several tools are used to interpret the data. Measures of central tendency represent the characteristics of the data, such as mean, median, and mode. Additionally, measures of variance like standard deviation and range are used to find the spread of data from the mean. Relative standing measures the distance between data locations. Commonly used measures of relative standings are percentile, z score, and quartiles.
Percentiles are a type of fractile that partition data into...

Statistical Analysis: Overview

Statistical Analysis: Overview

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...

You might also read

Related Articles

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

Sort by

Same author

BDIViz: An Interactive Visualization System for Biomedical Schema Matching with LLM-Powered Validation.

IEEE transactions on visualization and computer graphics·2025

Same author

TiVy: Time Series Visual Summary for Scalable Visualization.

IEEE transactions on visualization and computer graphics·2025

Same author

Design and Implementation of the Transparent, Interpretable, and Multimodal (TIM) AR Personal Assistant.

IEEE computer graphics and applications·2025

Same author

Multi-Field Visualization: Trait Design and Trait-Induced Merge Trees.

IEEE transactions on visualization and computer graphics·2025

Same author

Distributed Augmentation, Hypersweeps, and Branch Decomposition of Contour Trees for Scientific Exploration.

IEEE transactions on visualization and computer graphics·2024

Same author

Few-shot genes selection: subset of PAM50 genes for breast cancer subtypes classification.

BMC bioinformatics·2024

Search research articles

Related Experiment Video

Updated: Jun 28, 2026

Extracting Metrics for Three-dimensional Root Systems: Volume and Surface Analysis from In-soil X-ray Computed Tomography Data

Extracting Metrics for Three-dimensional Root Systems: Volume and Surface Analysis from In-soil X-ray Computed Tomography Data

Published on: April 26, 2016

Revisiting histograms and isosurface statistics.

Carlos E Scheidegger¹, John M Schreiner, Brian Duffy

¹Scientific Computing and Imaging Institute, University of Utah, Utah, USA. cscheid@sci.utah.edu

IEEE Transactions on Visualization and Computer Graphics

|November 8, 2008

Summary

This summary is machine-generated.

This study corrects discrepancies in analyzing isosurface geometric and data statistics. The new method ensures invariant statistical models for reliable algorithm evaluation and understanding data complexity.

More Related Videos

Longitudinal Micro-Computed Tomography Image Analysis for User-Defined Region of Interest in Critical-Sized Bone Defects

Longitudinal Micro-Computed Tomography Image Analysis for User-Defined Region of Interest in Critical-Sized Bone Defects

Published on: June 24, 2025

Analysis of Astrocyte Territory Volume and Tiling in Thick Free-Floating Tissue Sections

Analysis of Astrocyte Territory Volume and Tiling in Thick Free-Floating Tissue Sections

Published on: April 20, 2022

Related Experiment Videos

Last Updated: Jun 28, 2026

Extracting Metrics for Three-dimensional Root Systems: Volume and Surface Analysis from In-soil X-ray Computed Tomography Data

Extracting Metrics for Three-dimensional Root Systems: Volume and Surface Analysis from In-soil X-ray Computed Tomography Data

Published on: April 26, 2016

Longitudinal Micro-Computed Tomography Image Analysis for User-Defined Region of Interest in Critical-Sized Bone Defects

Longitudinal Micro-Computed Tomography Image Analysis for User-Defined Region of Interest in Critical-Sized Bone Defects

Published on: June 24, 2025

Analysis of Astrocyte Territory Volume and Tiling in Thick Free-Floating Tissue Sections

Analysis of Astrocyte Territory Volume and Tiling in Thick Free-Floating Tissue Sections

Published on: April 20, 2022

Area of Science:

Geometric measure theory
Data analysis
Scientific visualization

Background:

Recent studies link isosurface geometry to data statistics.
Existing methods have convergence and invariance issues.
These defects hinder reliable algorithm evaluation.

Purpose of the Study:

Explain discrepancies in isosurface statistical analysis.
Develop invariant statistical models for data.
Provide a consistent framework for algorithm evaluation.

Main Methods:

Applied Federer's Coarea Formula from geometric measure theory.
Developed weighted histograms using inverse gradient magnitude.
Formulated isosurface-preserving statistical models.

Main Results:

Explained discrepancies using geometric measure theory.
Introduced a novel, invariant statistical model.
Demonstrated noise as a cause for observed discrepancies in isosurface complexity.

Conclusions:

The Coarea Formula resolves issues in isosurface analysis.
The new statistical model offers consistent algorithm evaluation.
Corrected formulation aids in understanding data complexity and noise impact.