liveBook · Manning

Self-selection bias

Self-selection bias is systematic distortion caused when participation choices are related to participants’ characteristics or outcomes.

How 2 Manning books explain it

Effective Data Analysis

Self-selection bias is systematic distortion caused by differences between people who choose to participate in a study and those who do not.

How it is used
Frames participation differences as a limitation when interpreting self-report surveys and communicating findings to stakeholders.
How deep it goes
Defines the distortion and illustrates it with a survey example, but does not provide correction procedures.
Examples
Uses a stress and time-management survey whose busiest potential participants may respond less often.
What you can do after
Evaluate self-report findings by considering whether participants differ from nonparticipants in topic-relevant ways.

Read how Effective Data Analysis explains self-selection bias →

Grokking Statistics

Self-selection bias is distortion caused when individuals' choice to participate in a study is related to their characteristics or outcomes.

How it is used
Explains why voluntary participation can make a sample unrepresentative during study and survey design.
How deep it goes
Defines the bias through sleep, social-media, airline, and phone-poll examples, then stops short of statistical correction methods.
Examples
Uses dorm sleep studies, social-media recruitment, wearable ownership, airline polls, and workday political calls.
What you can do after
Diagnose likely participation gaps, disclose limitations, and plan mitigation during data collection.

Read how Grokking Statistics explains self-selection bias →

Related concepts

These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.