In published academic research, publication bias occurs when the outcome of an experiment or research study biases the decision to publish or otherwise distribute it. Publishing only results that show a significant finding disturbs the balance of findings in favor of positive results. The study of publication bias is an important topic in metascience. Despite similar quality of execution and design, papers with statistically significant results are three times more likely to be published than those with null results. This unduly motivates researchers to manipulate their practices to ensure statistically significant results, such as by data dredging. Many factors contribute to publication bias. For instance, once a scientific finding is well established, it may become newsworthy to publish reliable papers that fail to reject the null hypothesis. Most commonly, investigators simply decline to submit results, leading to non-response bias. Investigators may also assume they made a mistake, find that the null result fails to support a known finding, lose interest in the topic, or anticipate that others will be uninterested in the null results. The nature of these issues and the resulting problems form the five diseases that threaten science: "significosis, an inordinate focus on statistically significant results; neophilia, an excessive appreciation for novelty; theorrhea, a mania for new theory; arigorium, a deficiency of rigor in theoretical and empirical work; and finally, disjunctivitis, a proclivity to produce many redundant, trivial, and incoherent works." Attempts to find unpublished studies often prove difficult or are unsatisfactory. In an effort to combat this problem, some journals require studies submitted for publication pre-register (before data collection and analysis) with organizations like the Center for Open Science. Other proposed strategies to detect and control for publication bias include p-curve analysis and disfavoring small and non-randomized studies due to high susceptibility to error and bias. (Wikipedia).
This lesson reviews sources of bias when conducting a survey or poll. Site: http://mathispower4u.com
From playlist Introduction to Statistics
In this video, you’ll learn more about how to judge online information. Visit https://edu.gcfglobal.org/en/searchbetter/judging-online-information/1/ for our text-based lesson. This video includes information on what to ask yourself when reading a website including: • What is the site's p
From playlist Digital Media Literacy
How Ads and Clicks Shape the Internet
In this video, you’ll learn more about the impact advertisements have on internet users. For more information on this and other topics, visit https://edu.gcfglobal.org/en/topics/. We hope you enjoy!
From playlist Digital Media Literacy
If you are interested in learning more about this topic, please visit http://www.gcflearnfree.org/ to view the entire tutorial on our website. It includes instructional text, informational graphics, examples, and even interactives for you to practice and apply what you've learned.
From playlist Cover Letters
Statistics Lesson #4: Sources of Bias
This video is for my College Algebra and Statistics students (and anyone else who may find it helpful). I define bias, and we look at examples of different types of bias, including voluntary response bias, leading question bias, and sampling bias. I hope this is helpful! Timestamps: 0:00
From playlist Statistics
Confirmation Bias - Definition, Examples and How to Avoid - Psychology Motovlog
Learn the definition of the confirmation bias and understand examples of this cognitive bias in this informative video. The confirmatory bias is a very common flaw and can be found almost everywhere. There are a few tips you can use to avoid this common logical flaw in your daily thinking,
From playlist Cognitive Biases
See.Know.Bias - Using AI to Develop Media Literacy and Keep News Neutral | workshop capstone
Visit https://ai.science for more content like this, and to see the upcoming workshops! In this era of information overload, it is more important than ever to be a critical thinker and consumer of news media. See.Know.Bias is an app for detecting bias in news media, designed to develop me
From playlist Community Projects
The Blur Between Facts and Opinions in the Media
In this video, you’ll learn more about how the internet has helped blur the line between fact and opinion in the media. Pew study from the Pew Research Center, https://www.journalism.org/2018/06/18/distinguishing-between-factual-and-opinion-statements-in-the-news/ Visit https://www.gcfle
From playlist Digital Media Literacy
Publisher 2010: Printing and Publishing in Publisher 2010
In this video, you’ll learn more about printing and publishing in Publisher 2010. Visit https://www.gcflearnfree.org/publisher2010/producing-a-publication/1/ for our text-based lesson. This video includes information on: • Printing publications • Publishing publications • Publishing elect
From playlist Microsoft Publisher 2010
Stanford CS224N NLP with Deep Learning | Winter 2021 | Lecture 16 - Social & Ethical Considerations
For more information about Stanford's Artificial Intelligence professional and graduate programs visit: https://stanford.io/31ejtX7 To learn more about this course visit: https://online.stanford.edu/courses/cs224n-natural-language-processing-deep-learning To follow along with the course
From playlist Stanford CS224N: Natural Language Processing with Deep Learning | Winter 2021
Dismantling Algorithmic Bias with Patrick Ball, Brian Brackeen and Kristian Lum
We often hear of racist and biased algorithms, but what does it take to ensure the algorithms used to make decisions about potentially life-changing circumstances like bail and policing are fair? And what does fair even mean? Human Rights Data Analysis data scientists Patrick Ball and Kris
From playlist Math 498 - Algorithms in Social Context
Comment Responses: Bias? In My Algorithms? A Facebook News Story
Viewers like you help make PBS (Thank you 😃) . Support your local PBS Member Station here: https://to.pbs.org/donateidea Comment Responses! Tweet us! http://bit.ly/pbsideachanneltwitter Idea Channel Facebook! http://bit.ly/pbsideachannelfacebook Talk about this episode on reddit! http://
From playlist Comment Responses!
Multilayer Neural Networks - Part 2: Feedforward Neural Networks
This video is about Multilayer Neural Networks - Part 2: Feedforward Neural Networks Abstract: This is a series of video about multi-layer neural networks, which will walk through the introduction, the architecture of feedforward fully-connected neural network and its working principle, t
From playlist Neural Networks
Lecture: Scientific Method || PSY 380/Research Methods || Psych Streams w/ Dr. Swan
This video was for a remote class at the beginning of the Fall 2020 semester in Research Methods at Eureka College in Eureka, IL. It contains lecture material on a PowerPoint slideshow with me in the bottom left corner of the image. This was livestreamed on Twitch and edited down into the
From playlist Research Methods
Data Science Backpacking - Episode 2 - Data4Good
Interview with Miguel Luengo-Oroz Chief Data Scientist at UN Global Pulse
From playlist Data Science Backpacking
Only Tell Me the Good News - Bias in Research Publication
Subscribe to Healthcare Triage! https://bit.ly/2GlEYWG Publication bias is a big problem when it comes to health research. Researchers sometimes use spin or change the outcomes and goals to make their research seem positive, and the citation system in research literature amplifies the pro
From playlist Research
You and AI – the politics of AI
Kate Crawford, Distinguished Research Professor at New York University, a Principal Researcher at Microsoft Research New York, and the co-founder and co-director the AI Now Institute, discusses the biases built into machine learning, and what that means for the social implications of AI.
From playlist You and AI
Public Opinion: Crash Course Government and Politics #33
So today, Craig is finally going to start talking about politics. Now up until this point we've specifically been looking at government - that is answering the questions of who, what, and how in relation to policies. But politics is different in that it looks at why certain policies are ma
From playlist U.S. Government and Politics
A Geometric View on Private Gradient-Based Optimization
A Google TechTalk, presented by Steven Wu, 2021/04/16 ABSTRACT: Differential Privacy for ML Series. Deep learning models are increasingly popular in many machine learning applications where the training data may contain sensitive information. To provide formal and rigorous privacy guaran
From playlist Differential Privacy for ML
Bias? In My Algorithms? A Facebook News Story
Viewers like you help make PBS (Thank you 😃) . Support your local PBS Member Station here: https://to.pbs.org/donateidea Why Facebook News Can’t Escape Bias Tweet us! http://bit.ly/pbsideachanneltwitter Idea Channel Facebook! http://bit.ly/pbsideachannelfacebook Talk about this episode
From playlist Newest Episodes