Sampling bias
The sample differs systematically from the population.
What Is Sampling bias?
Sampling bias is a thinking trap where the sample differs systematically from the population.
How It Tricks You
It can make noisy, incomplete, or poorly framed data feel more meaningful than it is.
Real-World Example
A poll of morning commuters misses people who work nights.
Examples in Daily Life
Judging an entire neighborhood from conversations with the few neighbors who are usually outside.
Using feedback from the most engaged customers to decide what every customer wants.
Treating opinions in one social-media feed as a representative picture of public opinion.
Seen Online As
- Everyone I asked agrees with me.
- My feed makes this opinion look universal.
- The sample is convenient, not representative.
Sampling bias vs. Selection bias
Selection bias is the broader problem of who or what enters an analysis. Sampling bias is a specific form in which the resulting sample systematically differs from the population it is meant to represent.
How To Reduce Sampling bias
- Define the population before collecting examples.
- Ask who had no realistic chance of appearing in the sample.
- Compare the sample with known characteristics of the larger group.
What To Ask Instead
Does this sample match the group being discussed?
Related Thinking Traps
Common Situations
Related Reading
Evidence and context
Research Basis
Reviewed July 31, 2026
Established methodological problem in observational research and any claim based on a non-representative sample.
- An Introduction to Sample Selection Bias in Sociological DataR. A. Berk · Sociological Methods & Research · 1983
- Sample Selection Bias as a Specification ErrorJ. J. Heckman · Econometrica · 1979
Sources establish the research basis for this guide. The examples and check questions are plain-language applications by Thinking Traps.
Quick FAQ
What is Sampling bias?
The sample differs systematically from the population.
What is an example of Sampling bias?
A poll of morning commuters misses people who work nights.
How do I spot Sampling bias?
Does this sample match the group being discussed?
How can I reduce Sampling bias?
Define the population before collecting examples. Ask who had no realistic chance of appearing in the sample. Compare the sample with known characteristics of the larger group.