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

Review and Preview01:13

Review and Preview

9.8K
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...
9.8K
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

8.9K
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...
8.9K
Statistical Significance01:50

Statistical Significance

20.5K
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...
20.5K
Statgraphics01:10

Statgraphics

214
Statgraphics is a comprehensive statistical software suite designed for both basic and advanced data analysis. Originating in 1980 at Princeton University under Dr. Neil W. Polhemus, it was one of the pioneering tools for statistical computing on personal computers, with its public release in 1982 marking an early milestone in data science software. Over the years, it has evolved into a robust platform for data science, offering tools for regression analysis, ANOVA, multivariate statistics,...
214
Introduction to Statistics01:17

Introduction to Statistics

53.3K
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...
53.3K
Biostatistics: Overview01:20

Biostatistics: Overview

397
Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
397

You might also read

Related Articles

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

Sort by
Same author

From association to causation: interpreting propensity score-based analyses in real-world evidence.

Korean journal of anesthesiology·2026
Same author

Interpreting relative risks and odds ratios after propensity score matching: practical guidance for clinical research.

Korean journal of anesthesiology·2026
Same author

Impact of treatment goals on outcomes in critically ill nonagenarians: a retrospective observational study.

BMC geriatrics·2026
Same author

Percolation-Limited Threshold Switching in Strain-Graded Mott Devices.

ACS nano·2025
Same author

Epidural steroid injections for pain management in spinal metastases: clinical predictors of effectiveness in a retrospective cohort study.

Pain medicine (Malden, Mass.)·2025
Same author

Sweet relief: the role of glucopuncture in neuropathic pain management.

The Korean journal of pain·2025

Related Experiment Video

Updated: Oct 7, 2025

Multiplexed Fluorescent Immunohistochemical Staining, Imaging, and Analysis in Histological Samples of Lymphoma
07:52

Multiplexed Fluorescent Immunohistochemical Staining, Imaging, and Analysis in Histological Samples of Lymphoma

Published on: January 9, 2019

20.0K

The principles of presenting statistical results using figures.

Jae Hong Park1, Dong Kyu Lee2, Hyun Kang3

  • 1Department of Anesthesiology and Pain Medicine, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Korea.

Korean Journal of Anesthesiology
|January 12, 2022
PubMed
Summary

This study guides researchers on selecting appropriate figures for data visualization. It details essential components and provides examples for accurate and intuitive interpretation of statistical results.

Keywords:
Comparative studyFiguresGuidelinesPublication formatsResearch reportStatistics.

More Related Videos

Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation
07:12

Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation

Published on: August 26, 2016

9.6K
Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
10:58

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Published on: January 2, 2011

10.3K

Related Experiment Videos

Last Updated: Oct 7, 2025

Multiplexed Fluorescent Immunohistochemical Staining, Imaging, and Analysis in Histological Samples of Lymphoma
07:52

Multiplexed Fluorescent Immunohistochemical Staining, Imaging, and Analysis in Histological Samples of Lymphoma

Published on: January 9, 2019

20.0K
Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation
07:12

Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation

Published on: August 26, 2016

9.6K
Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
10:58

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Published on: January 2, 2011

10.3K

Area of Science:

  • Data Visualization
  • Scientific Communication
  • Statistical Analysis

Background:

  • Tables and figures are standard methods for presenting research data.
  • Effective figures enhance data interpretation through visualization.
  • Choosing the right figure type is crucial for accurate data representation.

Purpose of the Study:

  • To provide guidance on selecting appropriate figure types for research data.
  • To outline essential components for effective figure construction.
  • To illustrate best practices with examples of commonly used research figures.

Main Methods:

  • The article describes criteria for choosing suitable figure types.
  • It details the necessary components for creating informative figures.
  • Virtual data is used to demonstrate figure creation and interpretation.

Main Results:

  • Guidance is provided on selecting appropriate data visualization methods.
  • Key components for constructing effective figures are identified.
  • Examples illustrate the application of these principles in research.

Conclusions:

  • Proper figure selection and construction are vital for accurate data presentation.
  • Effective data visualization improves the interpretability of statistical results.
  • This guide aims to enhance the quality and clarity of research figures.