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

Genomics02:02

Genomics

38.9K
Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
38.9K
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

269
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
269
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

990
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:  
990
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

723
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:
723
Chi-square Analysis02:46

Chi-square Analysis

41.8K
The chi-square test is a statistical hypothesis test. It is used to check whether there is a significant difference between an expected value and an observed value. In the context of genetics, it enables us to either accept or reject a hypothesis, based on how much the observed values deviate from the expected values.
The chi-square test was developed by Pearson in 1990.
The first step of performing a Chi-square analysis is to establish a null hypothesis, which assumes that there is no real...
41.8K
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

1.2K
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...
1.2K

You might also read

Related Articles

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

Sort by
Same author

miRNA panel from HER2+ and CD24+ plasma extracellular vesicle subpopulations as biomarkers of early-stage breast cancer.

Breast cancer research : BCR·2025
Same author

Author Reply to "Anterior Cruciate Ligament Repair as One Approach in a Multifaceted Treatment Algorithm for the Management of Anterior Cruciate Ligament-Injured Patients".

Arthroscopy : the journal of arthroscopic & related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association·2025
Same author

Computational pathology assessments of cardiac stromal remodeling: Clinical correlates and prognostic implications in heart transplantation.

JHLT open·2025
Same author

Stress testing deep learning models for prostate cancer detection on biopsies and surgical specimens.

The Journal of pathology·2024
Same author

Optimized Whole-Slide-Image H&E Stain Normalization: A Step Towards Big Data Integration in Digital Pathology.

IEEE open journal of engineering in medicine and biology·2024
Same author

Editorial Commentary: Anterior Cruciate Ligament Primary Repair Has Limited Indications and Outcomes Inferior to Anterior Cruciate Ligament Reconstruction.

Arthroscopy : the journal of arthroscopic & related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association·2024
Same journal

Editorial Commentary: The Lateral Femoral Condyle Angle: Adding Another Piece to the Complex Puzzle of Anterior Cruciate Ligament Injury Risk.

Arthroscopy : the journal of arthroscopic & related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association·2026
Same journal

Lateral Femoral Condyle Angle Is an Anatomic Risk Factor for Anterior Cruciate Ligament Primary Injury and Secondary Graft Rerupture.

Arthroscopy : the journal of arthroscopic & related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association·2026
Same journal

Labral Hypoplasia by Preoperative Magnetic Resonance Imaging Predicts Higher Revision and Arthroplasty Risk After Hip Arthroscopy for Femoroacetabular Impingement Syndrome at 10 Year Follow-Up.

Arthroscopy : the journal of arthroscopic & related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association·2026
Same journal

Hip Arthroscopic Labral Repair for Femoroacetabular Impingement Yields High Return-to-Sport Rates and Improved Outcomes in Basketball Players.

Arthroscopy : the journal of arthroscopic & related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association·2026
Same journal

Editorial Commentary: Adding a Modified Lemaire Reconstruction to Anterior Cruciate Ligament Reconstruction Does Not Affect the Forgotten Joint Score-12 at 2 Years.

Arthroscopy : the journal of arthroscopic & related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association·2026
Same journal

Modified Lemaire Anterolateral Corner Reconstruction Does Not Impact Forgotten Joint Score-12 at 2 Years in Hamstring Graft Anterior Cruciate Ligament Reconstructions in High-Risk Patients.

Arthroscopy : the journal of arthroscopic & related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association·2026
See all related articles

Related Experiment Video

Updated: Nov 23, 2025

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
07:41

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

9.3K

Editorial Commentary: Big Databases Are Not All Created Equal: Interpret Their Studies With Caution.

Michael D Feldman

    Arthroscopy : the Journal of Arthroscopic & Related Surgery : Official Publication of the Arthroscopy Association of North America and the International Arthroscopy Association
    |January 1, 2021
    PubMed
    Summary
    This summary is machine-generated.

    Big data studies are vital in orthopedic research for large sample sizes. However, data quality and interpretation factors limit the generalizability of orthopedic big data findings.

    More Related Videos

    Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
    09:43

    Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

    Published on: November 22, 2019

    6.5K
    Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases
    05:02

    Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases

    Published on: October 24, 2019

    32.9K

    Related Experiment Videos

    Last Updated: Nov 23, 2025

    Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
    07:41

    Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

    Published on: May 17, 2019

    9.3K
    Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
    09:43

    Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

    Published on: November 22, 2019

    6.5K
    Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases
    05:02

    Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases

    Published on: October 24, 2019

    32.9K

    Area of Science:

    • Orthopedic Research
    • Health Informatics
    • Biostatistics

    Background:

    • Big data studies are crucial in orthopedics due to the impracticality of large randomized controlled trials.
    • The utility of big data hinges on the accuracy and completeness of the information within databases.

    Discussion:

    • Interpreting big data in orthopedics requires careful consideration of numerous confounding factors.
    • These factors include patient demographics (age, ethnicity, socioeconomic status), insurance, comorbidities, and regional variations.

    Key Insights:

    • Hospital status (inpatient/outpatient), data collection methods, and temporal changes in billing codes significantly impact results.
    • Clerical errors, recording biases, and the omission of key orthopedic outcome measures can distort findings.
    • Payer mix, population demographics, and catchment area characteristics influence the applicability of study results.

    Outlook:

    • Addressing data quality and interpretation challenges is essential for enhancing the reliability of orthopedic big data research.
    • Future research should focus on standardizing data collection and developing robust methodologies for bias correction.
    • Improving the generalizability of big data findings will ultimately benefit patient care and orthopedic treatment strategies.