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

Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

284
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:
284
Overview of Biostatistics in Health Sciences01:19

Overview of Biostatistics in Health Sciences

303
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...
303
Biostatistics: Overview01:20

Biostatistics: Overview

215
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...
215
Introduction to Statistics01:17

Introduction to Statistics

45.4K
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...
45.4K
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

478
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
478
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

102
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
102

You might also read

Related Articles

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

Sort by
Same author

Polygenic Prediction of Nongoal Response to Statin Therapy.

Circulation. Genomic and precision medicine·2026
Same author

Childhood residential proximity to the radiologically contaminated Coldwater Creek and later-life cardiovascular disease in the St. Louis Baby Tooth study.

The Science of the total environment·2026
Same author

Patient-clinician communication and cardiovascular outcomes: An analysis of the hispanic community health study/study of latinos (HCHS/SOL), 2008-2019.

American journal of preventive cardiology·2026
Same author

Waning light, waxing pain: The lunar cycle's association with migraine headache occurrence.

Headache·2025
Same author

Leveraging Datathons to Teach AI in Undergraduate Medical Education: Case Study.

JMIR medical education·2025
Same author

Migraine and sleep apnea, insomnia, and sleep patterns in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL).

Headache·2025

Related Experiment Video

Updated: May 26, 2025

Bridging the Technology Divide in the COVID-19 Era: Using Virtual Outreach to Expose Middle and High School Students to Imaging Technology
09:55

Bridging the Technology Divide in the COVID-19 Era: Using Virtual Outreach to Expose Middle and High School Students to Imaging Technology

Published on: September 28, 2022

1.6K

Shifting Towards Teaching How to Communicate Statistics in Medical Education.

Lathan Liou1, Murray A Mittleman2,3,4

  • 1Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY, USA.

Journal of Medical Education and Curricular Development
|February 25, 2025
PubMed
Summary

Medical education needs to shift from statistical applications to communication. Focusing on how clinicians explain statistics to patients can improve understanding and decision-making.

Keywords:
medical educationnumeracystatistical literacystatistics education

More Related Videos

Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide
09:52

Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide

Published on: January 15, 2017

17.1K
Using Mouse Mammary Tumor Cells to Teach Core Biology Concepts: A Simple Lab Module
10:39

Using Mouse Mammary Tumor Cells to Teach Core Biology Concepts: A Simple Lab Module

Published on: June 18, 2015

13.1K

Related Experiment Videos

Last Updated: May 26, 2025

Bridging the Technology Divide in the COVID-19 Era: Using Virtual Outreach to Expose Middle and High School Students to Imaging Technology
09:55

Bridging the Technology Divide in the COVID-19 Era: Using Virtual Outreach to Expose Middle and High School Students to Imaging Technology

Published on: September 28, 2022

1.6K
Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide
09:52

Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide

Published on: January 15, 2017

17.1K
Using Mouse Mammary Tumor Cells to Teach Core Biology Concepts: A Simple Lab Module
10:39

Using Mouse Mammary Tumor Cells to Teach Core Biology Concepts: A Simple Lab Module

Published on: June 18, 2015

13.1K

Area of Science:

  • Medical Education
  • Biostatistics
  • Health Communication

Background:

  • Medical school curricula include statistics, yet competency remains low.
  • Studies show medical residents and clinicians struggle with statistical concepts like confidence intervals and p-values.
  • High confidence in understanding statistical metrics does not correlate with actual knowledge.

Purpose of the Study:

  • Advocate for a paradigm shift in statistics education from application to communication.
  • Emphasize teaching statistical concepts as tools for patient communication.
  • Enhance patient understanding and decision-making through improved statistical communication skills.

Main Methods:

  • Perspective piece outlining a proposed shift in educational focus.
  • Discusses reframing classical statistical concepts for interpretation and communication.
  • Suggests future strategies for improving statistical education and communication.

Main Results:

  • Current statistics instruction inadequately prepares clinicians for patient communication.
  • Reframing statistical education can address learning barriers and improve understanding.
  • Proposed strategies aim to equip future clinicians with essential communication skills.

Conclusions:

  • A focus on statistical communication is crucial for evidence-based medicine.
  • Effective communication of statistical information empowers patients in healthcare decisions.
  • Revising curricula, exams, and accreditation can foster better statistical literacy in clinicians.