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Understand how sampling errors led to wrong election predictions and business decisions
Part of Data-Driven Decisions
Educational content slides
Literary Digest magazine's massive survey:
Actual election result: Franklin D. Roosevelt won by actual landslide (60.8% of vote)
What went wrong? They surveyed people from car registrations and phone directories. In 1936, only wealthy people owned cars and phones. Wealthy people voted Republican. The majority (poor/middle class) voted Democrat but weren't in the sample.
The lesson: Sample size doesn't matter if your sample is biased.**
Test your understanding with this quiz.
YouTube posts a Community poll asking 'Should we remove the dislike button?' 85% vote YES. What's the sampling problem?
Complete this exercise and get AI-powered feedback.
Sample bias is everywhere once you know to look for it—reviews, surveys, social media polls.
Think about: Who responds vs who doesn't? What groups are systematically excluded? How would you capture the silent majority?
This reveals: How to design better surveys and question any data based on biased samples.