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

Biostatistics: Overview01:20

Biostatistics: Overview

427
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...
427
Data: Types and Distribution01:19

Data: Types and Distribution

958
In biostatistics, data are the observations collected for analysis. There are two main types: parametric and non-parametric. Parametric data, which include continuous (e.g., weight) and discrete numerical data (e.g., number of tablets), assume a particular distribution pattern, often the normal distribution. Non-parametric data do not adhere to a specific distribution and typically comprise nominal (e.g., gender) and ordinal categorical data (e.g., pain scale ratings).
Distributions in...
958
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

642
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
642
Overview of Biostatistics in Health Sciences01:19

Overview of Biostatistics in Health Sciences

3.3K
Biostatistics involves the application of statistical techniques to scientific research in health-related fields, including biology and public health. These techniques are essential for designing studies, collecting data, and analyzing it to draw meaningful conclusions. Given the complexity of biological processes, particularly in studies involving human subjects, biostatistical methods are crucial for effectively organizing and interpreting data that might otherwise obscure underlying patterns...
3.3K
Introduction to Statistics01:17

Introduction to Statistics

56.3K
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...
56.3K
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

454
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
454

You might also read

Related Articles

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

Sort by
Same author

Polypharmacy burden and incident epilepsy among older adults in the United States.

Epilepsia open·2026
Same author

OMOP common data model transformation: leveraging a nationwide.

Journal of the American Medical Informatics Association : JAMIA·2026
Same author

Evaluation of steroids for acute COVID in the prevention of long COVID in children: An EHR and pediatric cohort study from the RECOVER Initiative.

PloS one·2026
Same author

The Association Between HPV Vaccination and Neighborhood-Level Social Deprivation Among Latinos by Nativity.

Journal of the American Board of Family Medicine : JABFM·2026
Same author

Efficacy of Short Course Cognitive Behavioral Therapy on Stress Reduction Among First-Year Surgical Residents.

Journal of surgical education·2026
Same author

The Paradoxical Association of Disaggregated Data Collection With Diabetes Control Among Latino Patients.

Medical care·2026
Same journal

The CALLY Index as a Predictive Tool for Surgical Site Infection After Pancreaticoduodenectomy: A Retrospective Cohort Study.

Surgical infections·2026
Same journal

Preoperative Prediction of Acute Appendicitis Pathological Type and Complicating Perforation Using IL-6 and IL-10.

Surgical infections·2026
Same journal

Outcomes and Predictors of Fungal Necrotizing Soft Tissue Infections.

Surgical infections·2026
Same journal

Light at the End of the Tunnel! Effects of 405 nm Visible Light Disinfection on Surgical Site Infection: A Prospective Randomized Cohort Study.

Surgical infections·2026
Same journal

Guideline-Directed Administration of Prophylactic Antibiotic-Loaded Bone Cement in Total Knee Arthroplasty Does Not Lower Infection Rate.

Surgical infections·2026
Same journal

Highlights from the 38th Annual Meeting of the Japanese Society of Surgical Infection.

Surgical infections·2026
See all related articles

Related Experiment Video

Updated: Oct 28, 2025

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

16.1K

Basic Introduction to Statistics in Medicine, Part 1: Describing Data.

Wyatt P Bensken1, Fredric M Pieracci2, Vanessa P Ho1,3

  • 1Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA.

Surgical Infections
|July 16, 2021
PubMed
Summary
This summary is machine-generated.

This article explains how to present data effectively for analysis. Understanding data types, properties, and distributions is crucial for accurate interpretation and hypothesis testing in research.

Keywords:
data descriptiondata sciencestatistics

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

14.8K
A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

357

Related Experiment Videos

Last Updated: Oct 28, 2025

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

16.1K
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

14.8K
A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

357

Area of Science:

  • Biostatistics
  • Data Science
  • Medical Informatics

Background:

  • Effective data presentation is fundamental for accurate analysis and interpretation in scientific research.
  • Standardized methods ensure clarity and reproducibility of findings.
  • Understanding data characteristics is a prerequisite for appropriate statistical analysis.

Purpose of the Study:

  • To review various types of data, their properties, and distributions.
  • To outline numerical and graphical methods for data presentation.
  • To emphasize the importance of data knowledge for hypothesis testing.

Main Methods:

  • Review of statistical principles for data presentation.
  • Categorization of variables into types (categorical, numerical) and subtypes (ordinal, nominal, ratio, interval).
  • Illustration using the National Inpatient Sample to differentiate emergency and non-emergency general surgery admissions.

Main Results:

  • Categorical data are presented as counts and percentages.
  • Numerical data require assessment for normality and properties like central tendency (mean, median, mode) and dispersion (standard deviation, range, interquartile range).
  • Histograms and box plots are recommended for initial graphical assessment of numerical data distribution.

Conclusions:

  • Knowledge of data types, distributions, and properties is essential for robust statistical analysis.
  • Appropriate data presentation facilitates subsequent interpretation and hypothesis testing.
  • The principles discussed are broadly applicable across various research domains.