Home > Library > Research Bias > What is Self-Selection Bias & How to Avoid it?

What is Self-Selection Bias & How to Avoid it?

Published by at July 18th, 2023 , Revised On January 23, 2024

We all have filled out multiple surveys and questionnaires in our college days. These surveys are conducted to gather data. The common problem is that not everyone can take that survey, meaning individuals select themselves whether they want to respond or not. People have all the authority of this decision.

Therefore, the researcher will miss a large segment of data. It happens as every survey is not accessible to everyone. This phenomenon is known as self-selection bias when people intentionally decide to participate in a study, survey, or research, which causes the data to be distorted. Let’s study self-selection bias in detail in this blog. 

What is Self-Selection Bias?

Here is a simple self-selection bias definition.

 ‘’Self-selection bias is the bias that occurs when individuals choose to be part of a study based on their likeness, motivations, or interests.’’

It is the discrimination that develops when people actively decide not to participate in a study or poll. Self-selection bias in qualitative research can distort research results by introducing bias into the sample. It could result in the over-representation or under-representation of particular groups or viewpoints, which biases the findings from the data. A few causes of self-selection bias are discussed below. 

What are the Causes of Self-Selection Bias?

There can be a lot of reasons for self-selection bias. Let’s discuss a few of them here:

Non-Response Bias

Non-response bias happens when a certain percentage of the population selected for a survey or study does not take part or answer, thus causing inaccurate results. 

Let’s take the scenario where a business emails customers to ask about their pleasure. Only a few customers answered, probably with different ideas than those who didn’t respond. The survey findings could show higher customer satisfaction levels if the non-respondents are unsatisfied consumers. 

Volunteer Bias

Volunteer bias affects studies that rely on volunteers or self-selected participants. 

For instance, people who willingly enrol in a study on the health impacts of a new food plan may already have a stronger interest in health and wellness. The results of this volunteer bias may exaggerate the beneficial effects of the diet on general health.

Response Bias

Response bias happens when respondents, deliberately or unintentionally, give replies that are not true, accurate, or impartial. 

For instance, due to social desirability bias and the desire to be satisfied, customers may give more favourable answers in a company’s customer satisfaction survey. Inflated satisfaction ratings that do not adequately reflect customers’ thoughts and experiences can result from this response bias.

Survivorship Bias

Survivorship bias occurs when insufficient data is available because it only comprises observations that “survived” a specific procedure or selection criterion. 

For instance, when examining investment performance, survivorship bias may manifest. It can draw biased conclusions that could result from focusing primarily on the profitable investment portfolios of wealthy people.

What are Some Examples of Self-Selection Bias?

Examples of Self-Selection Bias in Online Surveys

Online surveys are a common research tool but are susceptible to self-selection bias. Participants who voluntarily respond may have different characteristics or opinions than those who opt not to participate. 

An example of self-selection bias in surveys is that, suppose a survey about social media usage is conducted online. In that case, individuals who are more active or engaged in social media may be more inclined to respond, leading to overrepresenting heavy social media users.

Example of Self-Selection Bias in Clinical Trials

Another example of self-selection bias can be seen in clinical trials; individuals self-select to participate based on their willingness and eligibility. This can introduce self-selection bias as those who choose to participate may differ from the general population regarding health, age, or other relevant factors. 

For instance, if a study on a new medication for a specific condition relies on volunteers, the sample may not represent the broader population affected by the condition.

Example of Self-Selection Bias in Focus Groups

Focus groups gather small individuals to discuss specific topics or products. However, participants self-select based on their interests, availability, or personal experiences related to the topic. This can result in biased perspectives as the group may not represent the diversity of opinions or experiences in the larger population.

Example of Self-Selection Bias in Surveys with Incentives

Surveys that offer incentives, such as monetary rewards or gift cards, may attract individuals primarily motivated by the incentive rather than their genuine interest in the topic. Self-selection bias in research can be introduced when participants do not truly represent the target population, and their responses may differ from those who would have participated without the incentive.

Example of Self-Selection Bias in Support Groups

Support groups bring together individuals facing similar challenges or health conditions. However, participants self-select based on their willingness to seek support or their level of engagement with their condition. This can lead to biased perceptions of the condition or treatment outcomes, as the experiences and opinions of individuals who do not join the support group may be missed.

Example of Self-Selection Bias in Online Reviews

Online platforms often allow users to leave reviews for products, services, or experiences. However, individuals who voluntarily write reviews may have strong opinions or specific motivations that differ from the average user. This can introduce self-selection bias, where extreme positive or negative reviews may dominate, skewing the overall perception of the product or service.

Example of Self-Selection Bias in Political Polls

Political polls often rely on self-selected participants who choose to respond to surveys or participate in phone interviews. This can result in self-selection bias, as individuals who are more politically engaged or have stronger opinions may be more likely to participate. Consequently, the survey results may not accurately reflect the broader population’s political preferences.

Hire an Expert Editor

  • Precision and Clarity
  • Zero Plagiarism
  • Authentic Sources

How to Avoid Self-Selection Bias?

The following are some ways to avoid self-selection bias:

Random Sampling

Researchers can choose participants from the target demographic using random sampling techniques rather than relying solely on volunteers. Self-selection sampling bias cannot ensure that everyone is equally likely to be included.


By providing incentives, it is possible to boost participation rates and draw a wider variety of people. To avoid drawing participants who are just motivated by the rewards, researchers should carefully assess the type and magnitude of the incentives, as this can induce a new type of bias.

Targeted Recruitment

Trying to recruit participants from particular demographic groups helps an effort to recruit participants from particular demographic groups to assure a more diverse sample. Researchers can employ strategies to reach people interested in their work, such as targeted advertising, community outreach, or collaboration with appropriate organisations.

Unknown Participation

Assuring participants of anonymity or secrecy can allay privacy worries and promote a more open-minded and varied range of replies. People are less likely to self-select based on social desirability or judgmental fear when they feel more at ease discussing their thoughts or experiences.

Non-Intrusive Data Collection

Self-selection bias can be lessened by employing techniques that provide the least interruption to participants’ regular habits. For instance, approaches that gather data passively or online without effort or time commitment may draw a wider range of people.


Researchers can use statistical methods like post-stratification or weighting if self-selection bias persists despite efforts to reduce it. These techniques modify the data based on the target population’s known characteristics, ensuring that any biases produced by self-selection are considered in the final analysis.

Sensitivity Analysis

Researchers can use sensitivity analysis to evaluate the potential effects of self-selection bias on study outcomes. Researchers can determine the degree to which self-selection bias may have influenced the results by looking at various scenarios and assessing the reliability of findings.

Frequently Asked Questions

Self-selection bias is the discrimination that develops when people actively decide not to participate in a study or poll. A non-representative sample may come from this because those who self-select may have distinctive qualities or motivations that set them apart from the general population.

  • Online Surveys 
  • Clinical Trials 
  • Political Polls

Self-selection bias can distort research results since it introduces bias into the sample. It could result in the over- or under-representation of particular groups or viewpoints, which might bias the findings from the data.

To mitigate self-selection bias, researchers can use the following:

  • Random Sampling Techniques
  • Targeted Recruitment Methods
  • Post-Stratification
  • Conduct Sensitivity Analyses

About Owen Ingram

Avatar for Owen IngramIngram is a dissertation specialist. He has a master's degree in data sciences. His research work aims to compare the various types of research methods used among academicians and researchers.