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 Analysis: Overview01:11

Statistical Analysis: Overview

13.9K
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...
13.9K
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

1.3K
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...
1.3K
Overview of Minitab01:11

Overview of Minitab

500
Minitab is a statistical software package designed for data analysis. With its origins in the 1970s and development at Pennsylvania State University, Minitab has grown significantly in its capabilities and applications. It plays a crucial role in quality management projects, especially in Six Sigma initiatives, by offering tools for process improvement and statistical analysis. Minitab's significance lies in its user-friendly interface, making complex statistical analysis accessible to...
500
Statistical Methods to Analyze Parametric Data: ANOVA01:12

Statistical Methods to Analyze Parametric Data: ANOVA

1.4K
Analysis of Variance, or ANOVA, is a powerful statistical technique used to analyze parametric data, primarily in research and experimental studies. It's designed to compare the means of two or more groups, assisting researchers in identifying any significant differences between these group means. There are two main types of ANOVA based on the complexity of the analysis: one-way and two-way.
One-way ANOVA is applied when a single independent variable or factor is scrutinized. It compares...
1.4K
Biostatistics: Overview01:20

Biostatistics: Overview

654
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...
654
Statistical Package for the Social Sciences (SPSS)01:22

Statistical Package for the Social Sciences (SPSS)

1.0K
The Statistical Package for the Social Sciences, or SPSS, is a data management and analysis software suite. Developed by SPSS Inc. in 1968 and acquired by IBM in 2009, this tool was initially designed for social science data analysis, evolving to serve a wider range of disciplines. It was later renamed to Statistical Product and Service Solutions.
SPSS streamlines the process from data preparation to analysis and reporting. It is characterized by its user-friendly interface, which conceals...
1.0K

You might also read

Related Articles

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

Sort by
Same authorSame journal

DataAtlas: automatic generation of data dictionaries using large language models.

JAMIA open·2026
Same author

Physical restraint use in a United States intensive care unit-a retrospective cross sectional, single center cohort study from 2008 to 2022.

Lancet regional health. Americas·2026
Same author

Organ retrieval and collection of health information for donation: The ORCHID dataset.

Scientific data·2026
Same author

Development and International Evaluation of an Artificial Intelligence-based Model (PROGRxN-BCa) Using the World Health Organization 2004/2022 Grading System to Predict Progression Risk and Improve Substratification for Non-muscle-invasive Bladder Cancer.

European urology·2025
Same author

Raising awareness of potential biases in medical machine learning: Experience from a Datathon.

PLOS digital health·2025
Same author

Health Data Nexus: an open data platform for AI research and education in medicine.

GigaScience·2025
Same journal

An examination of the availability and characteristics of social needs data in the electronic health records: a path to social data harmonization and standardization at Johns Hopkins medicine.

JAMIA open·2026
Same journal

Generative artificial intelligence implementation in REDCap.

JAMIA open·2026
Same journal

Improving readability of layperson abstracts and summaries in oncology using task-specific large language model powered tool: results from the BRIDGE-AI 7 study.

JAMIA open·2026
Same journal

Accuracy of administrative data in ascertaining health conditions: a systematic review.

JAMIA open·2026
Same journal

Building a consumer health informatics introductory course consensus curriculum: an eDelphi study.

JAMIA open·2026
See all related articles

Related Experiment Video

Updated: Dec 30, 2025

A User-friendly and Powerful R Analysis of Large-scale Datasets
10:56

A User-friendly and Powerful R Analysis of Large-scale Datasets

Published on: November 4, 2025

231

tableone: An open source Python package for producing summary statistics for research papers.

Tom J Pollard1, Alistair E W Johnson1, Jesse D Raffa1

  • 1Massachusetts Institute of Technology (MIT), MIT Laboratory for Computational Physiology, Cambridge, Massachusetts, USA.

JAMIA Open
|January 28, 2020
PubMed
Summary
This summary is machine-generated.

Researchers can now generate reproducible summary statistics for studies using the Python package tableone. This open-source tool aids in improving data quality and reporting for quantitative research.

Keywords:
descriptive statisticspythonquantitative research

More Related Videos

Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study
07:50

Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study

Published on: April 18, 2025

779
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

33.3K

Related Experiment Videos

Last Updated: Dec 30, 2025

A User-friendly and Powerful R Analysis of Large-scale Datasets
10:56

A User-friendly and Powerful R Analysis of Large-scale Datasets

Published on: November 4, 2025

231
Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study
07:50

Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study

Published on: April 18, 2025

779
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

33.3K

Area of Science:

  • Computational statistics
  • Scientific software development

Background:

  • Quantitative research relies on accurate study population parameters for result interpretation.
  • Summary statistics, often presented in "Table 1", are crucial for understanding research data.

Purpose of the Study:

  • To introduce a simple, reproducible Python package for generating summary statistics.
  • To enhance the quality of reported summary statistics in research papers.

Main Methods:

  • The tableone package is developed using scientific computing best practices.
  • Code is open-source (MIT License) with a continuous integration testing framework.
  • Open issue tracking and public contributions are encouraged.

Main Results:

  • The tableone package automates summary statistics compilation into formats like CSV, HTML, and LaTeX.
  • Demonstrates application with MIMIC-III data via a Jupyter Notebook.
  • Includes outlier and modality tests (Tukey's rule, Hartigan's Dip Test) to identify data issues.

Conclusions:

  • Presents an open-source Python toolkit for reproducible research.
  • Aims to promote good data summarization practices.
  • Encourages use alongside visualization and statistical guidance.