Sampling Bias – Types, Examples & How to Avoid It

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Bias in research can affect the validity of the study, and it is important to avoid and correct it. It is important to have an understanding of how sampling bias emerges, the different types of bias, and how you can fix it. We’ll cover these subjects in this guide.

Sampling Bias – In a Nutshell

• Sampling bias occurs when certain samples are systematically more likely to be picked than others.
• It comes in different forms, including non-response, pre-screening bias, and survivorship bias.
• You can avoid sampling bias by using random number generators to select samples.
• You should ensure that all members in the sampling frame have an equal chance of participating in the study.

Definition: Sampling bias

Sampling bias is an issue where a sample is selected in such a way that certain members of the population have a higher or lower probability of being selected. This issue can make it harder to generalize the findings of the research, as it presents a threat to external validity or population validity.1

Sampling bias: Causes

It is important to understand the causes of sampling bias so that you avoid them when carrying out research. The problem results from the choice of data collection method and the selected research design.

Sampling bias in probability samples

Probability sampling is a system where every member has the same exact chance of being chosen.

Example:

You can use a random number generator to get a simple random sample, and this will give every member a known and equal chance of getting selected.

Probability sampling lowers sampling bias since none of the members has a higher or lower chance of being chosen. However, the researcher may still be biased when creating the sampling frame.

Example:

When running a survey research on the level of support for a nation’s dictator, the researcher may get more responses from supporters of the regime since opponents are afraid of airing their opinion. The researcher can use a random number generator to choose a sample from the respondents, and each of them will have a known and equal chance of being picked. However, the sampling frame itself will be biased.2

Sampling bias in non-probability samples

A non-probability sample is a technique where the researcher doesn’t use random methods to select the samples. The selection process of the sample is based on the subjective judgment of the researcher, and this makes it easy to introduce sampling bias.

Example:

When researching certain products, a researcher may use a sample that includes people from a specific region or school.2

Sampling bias: Types

 Type Explanation Example Self-selection A specific type of people are likely to participate in the research. Outgoing people are likely to participate in studies requiring their physical presence. Non-response This occurs when a certain type of people are more likely to refuse to participate in the study. When studying the impact of outdoor activities, people who rarely go outside may not respond. Undercoverage This occurs when a certain group isn’t adequately represented in the study. When studying the impacts of a drug, a researcher should adequately represent new users and older users of the medication. Survivorship This is where the samples that didn’t survive or pass are ignored or inadequately studied. A researcher studying winning tricks in casino (games may evaluate the winners while ignoring losers who apply similar tactics.4 Pre-screening or advertising The sample may be biased because of the method of screening participants. Also, how and where the study is advertised can affect the sample. In a study on the impact of workload and stress, only people with free time may be able to participate. This can lead to wrong conclusions. Healthy user This occurs in medical researches as healthy people are generally more likely to volunteer to test preventive medications or interventions. Volunteers for a medical program are likely to engage in regular exercise and are likely to abstain from drug abuse.3 5 6
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Sampling bias: Avoiding or correcting it

You can avoid and correct sampling bias by using the right research design and sampling process. Here are four methods of avoiding sampling bias:7

• Use simple random sampling or stratified sampling in the research as these do not depend on the judgment of the researcher.
• Ask the right questions to make sure every relevant response is recorded. If necessary, you should use open-ended questions to encourage participants to be more open.
• Make sure all potential respondents have an equal chance of participating in the study.
• Keep the surveys short and easy to access.
• Follow up on the respondents.8 9

Avoiding sampling bias: Oversampling

Oversampling is a good way of reducing sampling bias. With this method, the researcher selects more members from under-represented groups. Since this sample will not represent the population, the researcher has to apply weighting to the under-represented group. Oversampling allows researchers to shed light on groups that would be too small to report on.

An example of oversampling in a study on sexual assault would be the researcher oversampling groups that do not report assaults.

Example:

Sexual violence among black women rarely gets reported, so this group would need to be oversampled for this particular study.10

FAQs

What is sampling bias?

This bias occurs when a researcher is systematically more likely to pick certain samples over others.

How can a researcher eliminate sampling bias?

A researcher can eliminate sampling bias by giving all potential respondents an equal chance of taking part in the survey. They can also use simple random sampling.11

How does sampling bias affect research?

Bias poses a threat to external validity since the findings will be generalized to the population. It can lead to an overestimation or underestimation of the corresponding parameters in the population.

Does the sample size affect bias?

Sampling bias is commonly introduced by inappropriate sampling methods, so increasing the sample size will not help to lower the bias.

Sources

1 Formplus Blog. “Sampling Bias: Definition, Types + [Examples].” Formplus. July 27, 2022. https://www.formpl.us/blog/sampling-bias.

2 QuestionPro. “Probability sampling: What it is, Examples & Steps.” Accessed October 19, 2022, https://www.questionpro.com/blog/probability-sampling/.

3 Indeed Editorial Team. “What is a biased sample? (Examples and tips to avoid it).” Indeed. May 06, 2022. https://uk.indeed.com/career-advice/career-development/biased-sample.

4 Williams, Christopher and Jimin Khim. “Survivorship bias.” Brilliant. Accessed October 19, 2022. https://brilliant.org/wiki/survivorship-bias/.

5 VoxCo, “Undercoverage Bias: Causes, Examples, & more.” Accessed October 19, 2022. https://www.voxco.com/blog/undercoverage-bias-causes-examples-and-more/.

6 Glen, Stephanie. “Non Response Bias: Definition, Examples.” StatisticsHowTo.com. Accessed on October 19, 2022. https://www.statisticshowto.com/non-response-bias/.

7 Maden, Jack. “Sampling Bias: What It Is and 3 Ways to Avoid It.” CheckMarket. January, 2022. https://www.checkmarket.com/blog/sampling-bias-what-it-is-and-3-ways-to-avoid-it/.

8 Cleave, Philip. “Ways to Avoid Sampling Bias in Surveys.” SmartSurvey. September 24, 2019. https://www.smartsurvey.co.uk/blog/ways-to-avoid-sampling-bias-in-surveys.

9 QuestionPro. “Sampling Bias: What it is, Examples & How to Avoid It.” Accessed October 19, 2022. https://www.questionpro.com/blog/sampling-bias/.

10 Glen, Stephanie. “Oversampling in Statistics.” StatisticsHowTo.com. Accessed October 19, 2022. https://www.statisticshowto.com/oversampling-in-statistics/.

11 Berman, H. B. “Bias in Survey Sampling.” Stat Trek. Accessed October 19, 2022. https://stattrek.com/survey-research/survey-bias.

From

Salome Stolle

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Salome Stolle works as the brand manager for the English market at BachelorPrint. Throughout her 12-year residency in Denmark, she completed her International baccalaureate and Master’s in Culture, Communication, and Globalization with a specialization in media and market consumption. Through this experience, she has gained advanced competencies in academic writing and a high proficiency level in the English language. With her passion for writing, she does not only deliver well-written content but also strives to adjust to the students’ demands.