Survey methodology | Experimental bias
Participation bias or non-response bias is a phenomenon in which the results of elections, studies, polls, etc. become non-representative because the participants disproportionately possess certain traits which affect the outcome. These traits mean the sample is systematically different from the target population, potentially resulting in biased estimates. For instance, a study found that those who refused to answer a survey on AIDS tended to be "older, attend church more often, are less likely to believe in the confidentiality of surveys, and have lower sexual self disclosure." It may occur due to several factors as outlined in Deming (1990). Non-response bias can be a problem in longitudinal research due to attrition during the study. (Wikipedia).
This lesson reviews sources of bias when conducting a survey or poll. Site: http://mathispower4u.com
From playlist Introduction to Statistics
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
Survivorship Bias - Examples, Definitions, and String Art - Cognitive Biases
The Survivor Bias, also know as the survival or survivorship bias, is a commonly committed cognitive bias in the field of business and science. When people make assumptions from data without understanding where all the data is coming from, they are falling victim to a great example of a su
From playlist Cognitive Biases
Institutional bias explained by a mathematician - Indira Chatterji
IHES celebrated the 2022 International Day of Women in Mathematics through an event that took place on Tuesday, May 17. Indira Chatterji, professor of mathematics at the University Côte d’Azur, gave a talk entitled Institutional bias, explained by a mathematician. #womeninmaths #womeninmat
From playlist IHES celebrates women in mathematics
Linear regression (5): Bias and variance
Inductive bias; variance; relationship to over- & under-fitting
From playlist cs273a
Not all types of bias are fixed by diversifying your dataset
The idea of bias is often too general to be useful. There are several different types of bias, and different types require different interventions to try to address them. Through a series of cases studies, we will go deeper into some of the various causes of bias.
From playlist 11 Short Machine Learning Ethics Videos
Bias in an Artificial Neural Network explained | How bias impacts training
When reading up on artificial neural networks, you may have come across the term “bias.” It's sometimes just referred to as bias. Other times you may see it referenced as bias nodes, bias neurons, or bias units within a neural network. We're going to break this bias down and see what it's
From playlist Deep Learning Fundamentals - Intro to Neural Networks
STAT 200 Lesson 1 Lecture Video
Table of Contents: 00:00 - Introduction 00:52 - Learning Objectives 01:24 - 1. Identify cases and variables in a research study 02:30 - 2. Classify variables as categorical or quantitative 05:35 - 3. Identify explanatory and response variables in a research study 07:25 - 4. Distinguish b
From playlist STAT 200 Video Lectures
Algorithmic bias in healthcare AI: Scientific accuracy and social justice
This webinar will address a key social and ethical concern for Artificial Intelligence (AI) applications in healthcare: algorithmic bias, which occurs when automated decision-making results in a pattern of unfair or inequitable outcomes. In this webinar, we will present preliminary finding
From playlist Rachel Thomas videos
Sample bias: Response, Voluntary Response, Non-Response, Undercoverage, and Wording of Questions
From playlist Unit 4: Sampling and Experimental Design
Bias and Gender | Revision for Psychology A-Level or IB
I want to help you achieve the grades you (and I) know you are capable of; these grades are the stepping stone to your future. Even if you don't want to study science or maths further, the grades you get now will open doors in the future. Tutoring - We can match you with an experienced t
From playlist AQA A-Level Psychology | Revision Playlist
Statistics - 1.4 Critiquing a Published Study
We wrap up chapter 1 with how to critique a published study. Sorry for the boring video. You might just read this section instead. Power Point: https://bellevueuniversity-my.sharepoint.com/:p:/g/personal/kbrehm_bellevue_edu/Efhkc7f_gR1PoagzWR3mOxUBD8gCu7Q65lhBS2SK5W8bMQ?e=2850Vq This pl
From playlist Applied Statistics (Entire Course)
Stanford Seminar - Bias and Representation in Sociotechnical Systems
Danae Metaxa Stanford University May 14, 2021 Algorithms play a central role in our lives today, mediating our access to civic engagement, social connections, employment opportunities, news media and more. While the sociotechnical systems deploying these algorithms--search engines, social
From playlist Stanford Seminars
Examples of Selection Bias - Causal Inference
Today I talk about several distinct examples of selection bias.
From playlist Causal Inference - The Science of Cause and Effect
Google Keynote: Federated Learning & Federated Analytics-Research, Applications, & System Challenges
A Google TechTalk, presented by Hubert Eichner, Francoise Beaufay, Ravi Kumar & Peter Kairouz, 2021/11/9 ABSTRACT: An overview of federated analytics applications and algorithms, federated learning applications and algorithms, and how we build an infrastructure and scale it. About the S
From playlist 2021 Google Workshop on Federated Learning and Analytics
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!
Table of Content 1:20 Lesson 1 topics 2:08 Common terminology 3:52 Reliability & validity 6:12 Levels of measurement 9:30 Independent & dependent variables 11:22 Descriptive & inferential statistics 14:22 Experimental & observations designs 18:02 Causal conclusions 22:13 Control groups 26:
From playlist STAT 200 Lectures (OER)