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One of the most important things within the AP Stats course is the foundation of valid and reputable data; without it, there is no basis on which to formulate a solid conclusion.
Bias is a term given to any factor that may lead to data favoring one or more outcomes over others. There are several types of bias that you will cover throughout the course, but the four most common types that you will see during your time in the class are explained below with examples.
1. Response Bias 🙋♀️
- Response bias occurs when subjects polled produce an answer that does not truly reflect the population because of the situation.
- For instance, if an environmental science teacher asks his or her class students who recycles, the students are much less likely to answer truthfully to this figure of authority.
- This type of bias can severely skew data towards one response over others.
2. Voluntary Response ✋
- Voluntary response bias occurs when an individual, group, or company release a survey to a mass number of people, giving them the option to fill out and return or abstain from filling it out.
- Another common example can be seen on television. Some companies give the option to call-in and discuss a particular issue. Since calling in is completely voluntary, the company is very likely to hear from those individuals who feel strongly about the topic.
- Therefore, the conductors of the survey/sample are collecting unreputable data.
3. Non-response Bias (Population Bias) ❌
- Non-response bias, or population bias, typically occurs hand-in-hand with voluntary response bias. When people choose not to respond to a survey or sample, their opinions do not get evaluated.
- The people who do respond do not represent the entire population thus non-response bias can be detrimental to the results.
4. Undercoverage Bias 📞
- Undercoverage bias occurs when one or more populations is not represented appropriately through the selection and sampling procedures.
- For example, if a surveyor wanted to poll voters for an upcoming election and used a phone book to contact residents in his area, his survey would suffer from undercoverage bias.
- There are many reasons that undercoverage is present--one reason is that young adults (think 18-26) do not typically have landlines of their own.
- Another reason is that the extremely wealthy and extremely poor populations are not as likely to be listed in the phone book; all three of these populations were undercovered, so the results will show definite bias.