Selection Bias
The conclusion is a mistake because it assumes that everyone who saw the survey answered it. Maybe 1000 people saw the survey, and 900 of them thought, “I don’t like surveys, so I’m not going to fill it out.” These people wouldn’t have been included in the results.
This is an example of something called “selection bias”. This happens when you collect information in a way that doesn’t include everyone or everything that it should, which can lead to results that are inaccurate.
More Puzzles
Courses
Fallacy Detectors Part 1
Develop the skills to tackle logical fallacies through a series of 10 science-fiction videos with activities. Recommended for ages 8 and up.
A Statistical Odyssey
Learn about common mistakes in data analysis with an interactive space adventure. Recommended for ages 12 and up.
Symbolic Logic for Teens Part 1
Learn how to make sense of complicated arguments with 14 video lessons and activities. Recommended for ages 13 and up.
Emotional Intelligence
Learn to recognize, understand, and manage your emotions. Designed by child psychologist Ronald Crouch, Ph.D. Recommended for ages 5 and up.
Worksheets
Symbolic Logic Worksheets
Worksheets covering the basics of symbolic logic for children ages 12 and up.
Elementary School Worksheets and Lesson Plans
These lesson plans and worksheets teach students in grades 2-5 about superstitions, different perspectives, facts and opinions, the false dilemma fallacy, and probability.
Middle School Worksheets and Lesson Plans
These lesson plans and worksheets teach students in grades 5-8 about false memories, confirmation bias, Occam's razor, the strawman fallacy, and pareidolia.
High School Worksheets and Lesson Plans
These lesson plans and worksheets teach students in grades 8-12 about critical thinking, the appeal to nature fallacy, correlation versus causation, the placebo effect, and weasel words.
Statistical Shenanigans Worksheets and Lesson Plans
These lesson plans and worksheets teach students in grades 9 and up the statistical principles they need to analyze data rationally.