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What is Selection Bias – Types & Examples

Published by at July 31st, 2023 , Revised On October 5, 2023

Selection bias is a common phenomenon that affects the validity and generalisability of research findings. This bias creeps into research when the selection of participants is not representative of the entire population.

Let’s look at the selection bias definition in detail. 

What is Selection Bias?

Experts give selection bias meaning as 

‘’Selection bias refers to a systematic error or distortion in the process of selecting participants or samples for a study or analysis, resulting in a non-representative or biased sample. ‘’

It occurs when certain individuals or groups are more likely to be included or excluded from the sample, leading to inaccurate or misleading conclusions.

Selection bias can occur in various fields, including research, surveys, data analysis, and decision-making processes.

Selection Bias Example

A selection bias example is a study on the effectiveness of a new medication for a particular health condition that recruits participants only from a single clinic or hospital. If the participants from this clinic or hospital have access to better healthcare facilities and resources compared to the general population, it can introduce selection bias. 

The results of the study may overestimate the effectiveness of the medication because the participants selected are not representative of the broader population with the health condition.

In this scenario, individuals seeking treatment at the specific clinic or hospital may have more severe or complex cases, leading to potentially better outcomes compared to individuals who receive treatment elsewhere or do not seek treatment at all. The study’s findings would not accurately reflect the real-world effectiveness of the medication for the entire population affected by the health condition.

What are the Types of Selection Bias?

There are several types of selection bias that can occur in research and data analysis:

Self-Selection Bias

This bias occurs when individuals self-select to be part of a study or sample. It can lead to a non-random sample that may not represent the broader population accurately. For example, in surveys, individuals who feel strongly about a topic are more likely to participate, resulting in a biased sample.

Non-Response Bias

Non-response bias occurs when individuals selected to participate in a study or survey do not respond or choose not to participate. If those who do not respond differ systematically from those who do, the results may be biased. For instance, if a survey on income is only completed by individuals with higher incomes, it can lead to an overestimation of average income levels.

Volunteer Bias

Volunteer bias occurs when individuals voluntarily participate in a study or research. This can lead to a non-representative sample, as those who volunteer may possess certain characteristics or motivations that differ from the general population. For example, in clinical trials, volunteers may be more motivated or have better health than the average population.

Berkson’s Bias

Berkson’s bias is common in hospital-based studies. It arises when the study population is selected from a specific group, such as hospital patients, which may have a higher prevalence of certain conditions compared to the general population. This can result in an underestimation or overestimation of the association between variables.

Healthy User Bias

This bias occurs when a study population includes individuals who are more health-conscious or have healthier behaviours than the general population. This can lead to an overestimation of the benefits of certain interventions or treatments.

Overmatching Bias

Overmatching bias occurs when controls are selected based on characteristics that are influenced by exposure or outcome. This can result in an artificially strengthened association between the exposure and outcome of interest.

Diagnostic Access Bias

Diagnostic access bias occurs when the probability of being diagnosed with a condition depends on exposure status. This bias can distort the relationship between exposure and outcome if one group has better access to diagnostic tests than the other.

What is Selection Bias in Research?

Selection bias in research refers to the systematic error or distortion that occurs when the selection of participants or subjects for a study is not random or representative of the target population. It occurs when certain individuals or groups are more likely to be included or excluded from the study, leading to a biased sample.

Selection bias can arise at various stages of research, including participant recruitment, sampling, and data collection. It can impact the internal validity and generalisability of research findings, as the sample may not accurately represent the larger population of interest.

Selection bias can occur due to various factors, such as non-random sampling methods, self-selection by participants, differential response rates, or exclusion criteria that inadvertently exclude certain groups. These factors can introduce biases that influence the characteristics and outcomes observed in the study population.

What are the Examples of Selection Bias?

Examples of selection bias in daily life can include:

Online Product Reviews

When browsing online reviews, people tend to leave reviews for products they either strongly like or strongly dislike, leading to a biased representation of overall customer satisfaction.

Social Media Feeds

Social media algorithms often personalise content based on users’ past preferences and interactions, resulting in a biased selection of information that may reinforce existing beliefs and limit exposure to diverse perspectives.

Political Surveys

Surveys conducted by political organisations or campaigns may target specific demographics or party supporters, leading to a biased sample that may not accurately represent the views of the entire population.

Restaurant Ratings

People are more likely to leave reviews for restaurants when they have an exceptionally positive or negative experience, which can skew overall ratings and fail to capture the opinions of those who had average or neutral experiences.

Job Application Processes

Hiring managers may unintentionally exhibit selection bias by favouring candidates who come from certain schools or have similar backgrounds, overlooking potential talent from other sources.

Media Coverage

Media outlets often focus on sensational or controversial stories, resulting in a biased selection of news stories that may not accurately reflect the full range of events happening in the world.

Sampling Bias in Surveys

Surveys conducted in specific locations or targeting certain demographics may not capture the opinions and experiences of the broader population, leading to biased results.

It is important to recognise these examples of selection bias and be mindful of their potential impact on our understanding of the world. Seeking diverse sources of information and actively considering alternative perspectives can help mitigate the effects of selection bias in daily life.

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How to Avoid Selection Bias?

To avoid selection bias in research or data analysis, consider the following strategies:

Random Sampling

Use random sampling techniques to ensure that every individual or unit in the population has an equal chance of being selected for the study. This helps to create a representative sample and minimises selection bias.

Define Inclusion Criteria Carefully

Clearly define the criteria for selecting participants or subjects based on the research objectives. This helps to ensure that the selection process is based on relevant characteristics rather than personal biases or preferences.

Increase Response Rates

Take measures to increase response rates in surveys or studies to minimise non-response bias. Follow up with non-responders, offer incentives for participation, and ensure clear and concise communication about the importance and benefits of participation.

Use Stratified Sampling

If there are specific subgroups within the population that are of interest, employ stratified sampling to ensure adequate representation of each subgroup. This helps to prevent the under-representation or over-representation of particular groups.

Avoid Self-Selection

Minimise self-selection bias by actively recruiting participants rather than relying solely on voluntary participation. Reach out to potential participants through various channels, ensuring diversity in recruitment methods.

Consider Using Blinding

In certain studies, blinding the researchers to certain participant characteristics or group assignments can help minimise bias in participant selection and data analysis.

Validate Data Against External Sources

Validate the collected data against external sources or existing datasets to assess the representativeness of the sample and identify any potential biases.

Transparency in Reporting

Clearly describe the sampling methods, inclusion criteria, and any limitations related to participant selection in the research report. This transparency helps readers and reviewers evaluate the potential impact of selection bias on the study’s findings.

Frequently Asked Questions

Selection bias refers to a systematic error or distortion in research or data analysis that occurs when the selection of participants or samples is non-random or unrepresentative, leading to biased results.

There are several types of selection bias, including self-selection bias, non-response bias, volunteer bias, Berkson’s bias, healthy user bias, overmatching bias, and diagnostic access bias.

Selection bias can lead to skewed or inaccurate research findings by introducing a non-representative sample that does not accurately reflect the broader population of interest. It can undermine the internal validity and generalisability of study results.

Examples of selection bias can be observed in various contexts, such as online product reviews, social media feeds, political surveys, restaurant ratings, job application processes, media coverage, and sampling bias in surveys.

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.