Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Biostatistics: Overview01:20

Biostatistics: Overview

220
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...
220
Introduction to Statistics01:17

Introduction to Statistics

45.6K
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...
45.6K
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

6.1K
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...
6.1K
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

301
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:
301
Introduction to Nonparametric Statistics01:28

Introduction to Nonparametric Statistics

663
Nonparametric statistics offer a powerful alternative to traditional parametric methods, useful when assumptions about the population distribution cannot be made. Unlike parametric tests, which require data to follow a specific distribution with well-defined parameters (such as the mean and standard deviation), nonparametric tests do not require such constraints. This makes them particularly valuable when dealing with small sample sizes, skewed data, or ordinal and categorical variables.
One of...
663
Overview of Biostatistics in Health Sciences01:19

Overview of Biostatistics in Health Sciences

320
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...
320

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Observed Mealtime Interactions, Meal Healthfulness, and Childhood Obesity Among Low-Income Families.

Pediatric obesity·2026
Same author

Comparing neuroprotective drug efficacy in rodent neonatal brain injury models.

Pediatric research·2026
Same author

The Diabetic Foot Consortium Biomarker Platform Study: A New Approach to Advance Diabetic Foot Ulcer Healing.

Advances in wound care·2026
Same author

Antihypertensive Combinations Modify Cardiovascular Risk Factor Importance: A Machine Learning Analysis of the ACCOMPLISH Trial.

Journal of clinical hypertension (Greenwich, Conn.)·2026
Same author

Partnering with Communities to Improve Diabetic Foot Ulcer Care in High-Risk Populations.

Advances in wound care·2026
Same author

Feasibility Pilot Trial for the Trajectories of Recovery after Intravenous Propofol versus Inhaled Volatile Anesthesia (THRIVE) Pragmatic Randomized Clinical Trial.

Anesthesiology·2026
Same journal

Mapping the landscape of dissemination and implementation science across the CTSA consortium: A multi-domain environmental website scan.

Journal of clinical and translational science·2026
Same journal

Are you still engaged? Leveraging the LMS to support engagement in asynchronous courses.

Journal of clinical and translational science·2026
Same journal

Prediction of α-synuclein seed amplification assay positivity in remotely followed <i>LRRK2</i> G2019S carriers using a validated data-driven model.

Journal of clinical and translational science·2026
Same journal

Embracing complexity: An opportunity for developmental evaluation.

Journal of clinical and translational science·2026
Same journal

Artificial intelligence in clinical trial participant recruitment and retention: A scoping review and meta-analysis.

Journal of clinical and translational science·2026
Same journal

Steps toward developing an algorithm to facilitate recognition of translational science.

Journal of clinical and translational science·2026
查看所有相关文章

相关实验视频

Updated: Jun 5, 2025

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.3K

为全职员工统计学家开发贝叶斯式工作坊.

Shokoufeh Khalatbari1, Veera Baladandayuthapani2, Niko Kaciroti2

  • 1The Michigan Institute for Clinical and Health Research, University of Michigan, Ann Arbor, MI, USA.

Journal of clinical and translational science
|December 10, 2024
PubMed
概括
此摘要是机器生成的。

一个研讨会提高了生物统计学家的贝叶斯推理技能. 基于项目的学习提高了对应用贝叶斯方法的理解和信心,解决了关键的知识差距.

关键词:
应用生物统计科学网络.贝叶斯的方法 贝叶斯的方法临床和翻译科学奖 (CTSA)密歇根州临床和健康研究研究所 (MICHR)在 R 编程中使用 R 编程.课程评价 课程评价定制培训 定制培训 定制培训翻译研究是翻译研究.翻译科学是翻译科学.劳动力发展 劳动力发展

更多相关视频

A Quantitative Fitness Analysis Workflow
11:39

A Quantitative Fitness Analysis Workflow

Published on: August 13, 2012

14.4K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.3K

相关实验视频

Last Updated: Jun 5, 2025

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.3K
A Quantitative Fitness Analysis Workflow
11:39

A Quantitative Fitness Analysis Workflow

Published on: August 13, 2012

14.4K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.3K

科学领域:

  • 生物统计学 生物统计学
  • 统计推理 统计推理
  • 计算统计学 计算统计学

背景情况:

  • 频率主义和贝叶斯统计推理方法在使用先前知识方面有所不同.
  • 贝叶斯方法虽然具有历史意义,但由于计算限制和理论辩论,在采用时面临挑战.
  • 越来越多的计算能力和专注于结合先前的知识,刺激了对贝叶斯方法的重新兴趣.

研究的目的:

  • 解决培训主要在频率主义方法的生物统计学家之间的贝叶斯方法技能差距.
  • 开发和评估一个实用,可访问的培训计划,用于应用贝叶斯技术.

主要方法:

  • 为全职生物统计工作人员设计了一系列定制的基于项目的工作坊.
  • 培训强调了沉浸式,实践式的学习,以适应工作时间安排.
  • 通过参与者调查和关键项目的评估来评估计划的影响.

主要成果:

  • 所有20名参与者都成功完成了研讨会.
  • 调查结果显示,参与者对贝叶斯理论的理解和对应用这些方法的信心显著增加.
  • 基石项目证实了参与者实施贝叶斯方法的能力.

结论:

  • 研讨会有效地提高了生物统计学家对贝叶斯方法的实际技能和信心.
  • 基于项目的,适应时间表的格式促进了充分参与和成功获得技能.
  • 参与者现在可以更好地在他们的专业工作中应用贝叶斯式技术.