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

Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

2.0K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
2.0K
Data Collection by Survey01:07

Data Collection by Survey

7.0K
The systematic method of obtaining and analyzing accurate information of a population is called data collection. A survey is a standard method of data collection that involves collecting information from a target human population about their experience, opinion, or knowledge of a product, service, or process. The responses are recorded and interpreted. The most common survey examples are written questionnaires, face-to-face or telephonic conversations, focus groups, and electronic (e-mail or...
7.0K
Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

2.3K
In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
2.3K
Convenience Sampling Method00:55

Convenience Sampling Method

9.6K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population.
Convenience sampling is a non-random method of sample selection; this method selects individuals that are easily accessible and may result in biased data. For example, a marketing...
9.6K
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

6.4K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
6.4K
Bias01:22

Bias

4.9K
Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
4.9K

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

Competition in the Segregation Mechanism of Granular Flow Within a 2D Rotating Drum Based on Magnetic Positioning Technology.

Sensors (Basel, Switzerland)·2026
Same author

Dose Responses to Supplemental Polyacrylamide on Digestion, Metabolism, and Ruminal Digestive-Enzyme Activities in Cattle.

Life (Basel, Switzerland)·2025
Same author

Publisher Correction: Data quality in crowdsourcing and spamming behavior detection.

Behavior research methods·2025
Same author

Attribution of vegetation changes in China based on improved residual trend method.

Ying yong sheng tai xue bao = The journal of applied ecology·2025
Same author

TREM2 and Pain Development: An Old Molecule, a New Target.

Journal of neurochemistry·2025
Same author

Positive Emotion Enhances Memory by Promoting Memory Reinstatement across Repeated Learning.

The Journal of neuroscience : the official journal of the Society for Neuroscience·2025

相关实验视频

Updated: Sep 12, 2025

A Cross-Disciplinary and Multi-Modal Experimental Design for Studying Near-Real-Time Authentic Examination Experiences
08:33

A Cross-Disciplinary and Multi-Modal Experimental Design for Studying Near-Real-Time Authentic Examination Experiences

Published on: September 4, 2019

7.1K

众包和垃圾邮件行为检测中的数据质量.

Yang Ba1, Michelle V Mancenido2, Erin K Chiou3

  • 1Ira A. Fulton Schools of Engineering, School of Computing and Augmented Intelligence, Data Science, Analytics and Engineering, Arizona State University, Suite 342AE, 3rd floor 699 S. Mill Avenue, 85281, Tempe, AZ, USA. yangba@asu.edu.

Behavior research methods
|August 8, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的方法来评估众包数据质量和检测垃圾邮件发送者. 它通过评估注释器的一致性和可信度来增强机器学习,这对于可靠的AI开发至关重要.

关键词:
众包平台是一个众包平台.数据质量数据质量数据质量一般化的随机效应模型.衡量指标 衡量指标 衡量指标 衡量指标垃圾邮件行为.统计学假设测试 统计学假设测试

更多相关视频

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community
08:53

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

Published on: May 31, 2019

5.3K
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.8K

相关实验视频

Last Updated: Sep 12, 2025

A Cross-Disciplinary and Multi-Modal Experimental Design for Studying Near-Real-Time Authentic Examination Experiences
08:33

A Cross-Disciplinary and Multi-Modal Experimental Design for Studying Near-Real-Time Authentic Examination Experiences

Published on: September 4, 2019

7.1K
Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community
08:53

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

Published on: May 31, 2019

5.3K
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.8K

科学领域:

  • 机器学习 机器学习
  • 数据科学数据科学数据科学
  • 人工智能的人工智能

背景情况:

  • 众包对于有效地标记机器学习数据集至关重要.
  • 评估群众提供的数据质量对于减少偏见和提高AI性能至关重要.
  • 传统的质量指标对于复杂的在线众包场景是不够的.

研究的目的:

  • 开发一种系统的方法来评估众包数据质量.
  • 检测和分类来自群众工作者的垃圾邮件威胁.
  • 为了测量注释者的一致性和可信度,而没有基本真相.

主要方法:

  • 差异分解用于数据质量评估和垃圾邮件检测.
  • 垃圾邮件发送者被分为三个行为类别.
  • 开发一个垃圾邮件索引,以确保整体数据的一致性.
  • 使用马尔科夫链和通用随机效应模型来衡量员工信任度.

主要成果:

  • 展示了评估众包数据质量的实际框架.
  • 提出的方法有效地识别和分类了垃圾邮件发送者.
  • 这些技术在使用真实和模拟数据的面部验证任务中被证明是有利的.

结论:

  • 开发的系统方法提高了机器学习众包数据的可靠性.
  • 准确评估注释者的一致性和可信度是可以实现的,即使没有基础真相.
  • 这种方法对于减轻偏见和提高在众包数据上训练的AI模型性能至关重要.