What is Sampling bias?
Sampling bias occurs when the sample used in a study is not representative of the population to which the findings will be generalized. It produces results that are true for the sample but misleading about the broader population.
How it works
Common forms include self-selection bias (people who volunteer for studies differ from those who do not), survivor bias (studying only those who survived a process while ignoring those who dropped out), convenience sampling (using readily available participants like college students), and non-response bias (people who respond to surveys differ from those who do not). Henrich et al. (2010) noted that most psychology research is conducted on WEIRD samples (Western, Educated, Industrialized, Rich, Democratic), which represent less than 15% of the world population.
Applied example
A customer satisfaction survey with a 10% response rate likely overrepresents customers with strong opinions (very satisfied or very dissatisfied) while missing the moderate majority. Basing product decisions on this biased sample can lead to changes that serve vocal minorities rather than the typical customer.
Why it matters
Sampling bias means research findings may describe who was studied rather than the population of interest, making sample characteristics a critical caveat for every behavioral science claim.
