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GuidesResearch methodsUnderstanding the different types of bias in research (2024 guide)

Understanding the different types of bias in research (2024 guide)

Last updated

6 October 2023


Claire Bonneau

All research projects are vulnerable to bias, no matter the topic or industry. But are you aware of the most common types of bias that may be jeopardizing the accuracy of your efforts?

Research bias is an invisible force that overly highlights or dismisses the chosen study topic’s traits. When left unchecked, it can significantly impact the validity and reliability of your research.

In a perfect world, every research project would be free of any trace of bias—but for this to happen, you need to be aware of the most common types of research bias that plague studies.

Read this guide to learn more about the most common types of bias in research and what you can do to design and improve your studies to create high-quality research results.

What is research bias?

Research bias is the tendency for qualitative and quantitative research studies to contain prejudice or preference for or against a particular group of people, culture, object, idea, belief, or circumstance.

Bias is rarely based on observed facts. In most cases, it results from societal stereotypes, systemic discrimination, or learned prejudice.

Every human develops their own set of biases throughout their lifetime as they interact with their environment. Often, people are unaware of their own biases until they are challenged—and this is why it’s easy for unintentional bias to seep into research projects.

Left unchecked, bias ruins the validity of research. So, to get the most accurate results, researchers need to know about the most common types of research bias and understand how their study design can address and avoid these outcomes.

The two primary types of bias

Historically, there are two primary types of bias in research:

Conscious bias

Conscious bias is the practice of intentionally voicing and sharing a negative opinion about a particular group of people, beliefs, or concepts.

Characterized by negative emotions and opinions of the target group, conscious bias is often defined as intentional discrimination.

In most cases, this type of bias is not involved in research projects, as they are unjust, unfair, and unscientific.

Unconscious bias

An unconscious bias is a negative response to a particular group of people, beliefs, or concepts that is not identified or intentionally acted upon by the bias holder.

Because of this, unconscious bias is incredibly dangerous. These warped beliefs shape and impact how someone conducts themselves and their research. The trouble is that they can’t identify the moral and ethical issues with their behavior.

Examples of commonly occurring research bias

Humans use countless biases daily to quickly process information and make sense of the world. But, to create accurate research studies and get the best results, you must remove these biases from your study design.

Here are some of the most common types of research biases you should look out for when planning your next study:

Information bias

During any study, tampering with data collection is widely agreed to be bad science. But what if your study design includes information biases you are unaware of?

Also known as measurement bias, information bias occurs when one or more of the key study variables are not correctly measured, recorded, or interpreted. As a result, the study’s perceived outcome may be inaccurate due to data misclassification, omission, or obfuscation (obscuring). 

Observer bias

Observer bias occurs when researchers don’t have a clear understanding of their own personal assumptions and expectations. During observational studies, it’s possible for a researcher’s personal biases to impact how they interpret the data. This can dramatically affect the study’s outcome.

The study should be double-blind to combat this type of bias. This is where the participants don’t know which group they are in, and the observers don’t know which group they are observing.

Regression to the mean (RTM)

Bias can also impact research statistics.

Regression of the mean (RTM) refers to a statistical bias that if a first clinical reading is extreme in value (i.e., it’s very high or very low compared to the average), the second reading will provide a more statistically normal result.

Here’s an example: you might be nervous when a doctor takes your blood pressure in the doctor’s surgery. The first result might be quite high. This is a phenomenon known as “white coat syndrome.” When your blood pressure is retaken to double-check the value, it is more likely to be closer to typical values.

So, which value is more accurate, and which should you record as the truth?

The answer depends on the specific design of your study. However, using control groups is usually recommended for studies with a high risk of RTM.

Performance bias

A performance bias can develop if participants understand the study’s nature or desired outcomes. This can harm the study’s accuracy, as participants may adjust their behavior outside of their normal to improve their performance. This results in inaccurate data and study results.

This is a common bias type in medical and health studies, particularly those studying the differences between two lifestyle choices.

To reduce performance bias, researchers should strive to keep members of the control and study groups unaware of the other group’s activities. This method is known as “blinding.”

Recall bias

How good is your memory? Chances are, it’s not as good as you think—and the older the memory, the more inaccurate and biased it will become.

A recall bias commonly occurs in self-reporting studies requiring participants to remember past information. While people can remember big-picture events (like the day they got married or landed their first job), routine occurrences like what they do after work every Tuesday are harder to recall.

To offset this type of bias, design a study that engages with participants on both short- and long-term periods to help keep the content more top of mind.

Researcher bias

Researcher bias (also known as interviewer bias) occurs due to the researcher’s personal beliefs or tendencies that influence the study’s results or outcomes.

These types of biases can be intentional or unintentional, and most are driven by personal feelings, historical stereotypes, and assumptions about the study’s outcome before it has even begun.

Question order bias

Survey design and question order is a huge area of contention for researchers. These elements are essential for quality study design and can prevent or invite answer bias.

When designing a research study that collects data via survey questions, the order of the questions presented can impact how the participants answer each subsequent question. Leading questions (questions that guide participants toward a particular answer) are perfect examples of this. When included early in the survey, they can sway a participant’s opinions and answers as they complete the questionnaire.

This is known as systematic distortion, meaning each question answered after the guiding questions is impacted or distorted by the wording of the questions before.

Demand characteristics

Body language and social cues play a significant role in human communication—and this also rings true for the validity of research projects

A demand characteristic bias can occur due to a verbal or non-verbal cue that encourages research participants to behave in a particular way.

Imagine a researcher is studying a group of new grad business students about their experience applying to new jobs one, three, and six months after graduation. They scowl every time a participant mentions they don’t use a cover letter. This reaction may encourage participants to change their answers, harming the study’s outcome and resulting in less accurate results.

Courtesy bias

Courtesy bias arises from not wanting to share negative or constructive feedback or answers—a common human tendency.

You’ve probably been in this situation before. Think of a time when you had a negative opinion or perspective on a topic, but you felt the need to soften or reduce the harshness of your feedback to prevent someone’s feelings from being hurt.

This type of bias also occurs in research. Without a comfortable and non-judgmental environment that encourages honest responses, courtesy bias can result in inaccurate data intake.

Studies based on small group interviews, focus groups, or any in-person surveys are particularly vulnerable to this type of bias because people are less likely to share negative opinions in front of others or to someone’s face.

Extreme responding

Extreme responding refers to the tendency for people to respond on one side of the scale or the other, even if these extreme answers don’t reflect their true opinion. 

This is a common bias in surveys, particularly online surveys asking about a person’s experience or personal opinions (think questionnaires that ask you to decide if you strongly disagree, disagree, agree, or strongly agree with a statement).

When this occurs, the data will be skewed. It will be overly positive or negative—not accurate. This is a problem because the data can impact future decisions or study outcomes.

Writing different styles of questions and asking for follow-up interviews with a small group of participants are a few options for reducing the impact of this type of bias.

Social desirability bias

Everyone wants to be liked and respected. As a result, societal bias can impact survey answers.

It’s common for people to answer questions in a way that they believe will earn them favor, respect, or agreement with researchers. This is a common bias type for studies on taboo or sensitive topics like alcohol consumption or physical activity levels, where participants feel vulnerable or judged when sharing their honest answers.

Finding ways to comfort participants with ensured anonymity and safe and respectful research practices are ways you can offset the impact of social desirability bias.

Selection bias

For the most accurate results, researchers need to understand their chosen population before accepting participants. Failure to do this results in selection bias, which is caused by an inaccurate or misrepresented selection of participants that don’t truly reflect the chosen population.

Self-selection bias

To collect data, researchers in many studies require participants to volunteer their time and experiences. This results in a study design that is automatically biased toward people who are more likely to get involved.

People who are more likely to voluntarily participate in a study are not reflective of the common experience of a broad, diverse population. Because of this, any information collected from this type of study will contain a self-selection bias.

To avoid this type of bias, researchers can use random assignment (using control versus treatment groups to divide the study participants after they volunteer).

Sampling or ascertainment bias

When choosing participants for a study, take care to select people who are representative of the overall population being researched. Failure to do this will result in sampling bias.

For example, if researchers aim to learn more about how university stress impacts sleep quality but only choose engineering students as participants, the study won’t reflect the wider population they want to learn more about.

To avoid sampling bias, researchers must first have a strong understanding of their chosen study population. Then, they should take steps to ensure that any person within that population has an equal chance of being selected for the study.

Attrition bias

People tend to be hard on themselves, so an attrition bias toward the impact of failure versus success can seep into research.

Many people find it easier to list things they struggle with rather than things they think they are good at. This also occurs in research, as people are more likely to value the impact of a negative experience (or failure) than that of a positive, successful outcome.

Survivorship bias

In medical clinical trials and studies, a survivorship bias may develop if only the results and data from participants who survived the trial are studied. Survivorship bias also includes participants who were unable to complete the entire trial, not just those who passed away during the duration of the study.

In long-term studies that evaluate new medications or therapies for high-mortality diseases like aggressive cancers, choosing to only consider the success rate, side effects, or experiences of those who completed the study eliminates a large portion of important information. This disregarded information may have offered insights into the quality, efficacy, and safety of the treatment being tested.

Nonresponse bias

A nonresponse bias occurs when a portion of chosen participants decide not to complete or participate in the study. This is a common issue in survey-based research (especially online surveys).

In survey-based research, the issue of response versus nonresponse rates can impact the quality of the information collected. Every nonresponse is a missed opportunity to get a better understanding of the chosen population, whether participants choose not to reply based on subject apathy, shame, guilt, or a lack of skills or resources.

To combat this bias, improve response rates using multiple different survey styles. These might include in-person interviews, mailed paper surveys, and virtual options. However, note that these efforts will never completely remove nonresponse bias from your study.

Cognitive bias

Cognitive biases result from repeated errors in thinking or memory caused by misinterpreting information, oversimplifying a situation, or making inaccurate mental shortcuts. They can be tricky to identify and account for, as everyone lives with invisible cognitive biases that govern how they understand and interact with their surrounding environment.

Anchoring bias

When given a list of information, humans have a tendency to overemphasize (or anchor onto) the first thing mentioned.

For example, if you ask people to remember a grocery list of items that starts with apples, bananas, yogurt, and bread, people are most likely to remember apples over any of the other ingredients. This is because apples were mentioned first, despite not being any more important than the other items listed.

This habit conflates the importance and significance of this one piece of information, which can impact how you respond to or feel about the other equally important concepts being mentioned.

Halo effect

The halo effect explains the tendency for people to form opinions or assumptions about other people based on one specific characteristic. Most commonly seen in studies about physical appearance and attractiveness, the halo effect can cause either a positive or negative response depending on how the defined trait is perceived.

Framing effect

Framing effect bias refers to how you perceive information based on how it’s presented to you. 

To demonstrate this, decide which of the following desserts sounds more delicious.

  1. “Made with 95% natural ingredients!”

  2. “Contains only 5% non-natural ingredients!”

Both of these claims say the same thing, but most people have a framing effect bias toward the first claim as it’s positive and more impactful.

This type of bias can significantly impact how people perceive or react to data and information.

The misinformation effect

The misinformation effect refers to the brain’s tendency to alter or misremember past experiences when it has since been fed inaccurate information. This type of bias can significantly impact how a person feels about, remembers, or trusts the authority of their previous experiences.

Confirmation bias

Confirmation bias occurs when someone unconsciously prefers or favors information that confirms or validates their beliefs and ideas.

In some cases, confirmation bias is so strong that people find themselves disregarding information that counters their worldview, resulting in poorer research accuracy and quality.

We all like being proven right (even if we are testing a research hypothesis), so this is a commonly occurring cognitive bias that needs to be addressed during any scientific study.

Availability heuristic

All humans contextualize and understand the world around them based on their past experiences and memories. Because of this, people tend to have an availability bias toward explanations they have heard before. 

People are more likely to assume or gravitate toward reasoning and ideas that align with past experience. This is known as the availability heuristic. Information and connections that are more available or accessible in your memory might seem more likely than other alternatives. This can impact the validity of research efforts.

How to avoid bias in your research

Research is a compelling, complex, and essential part of human growth and learning, but collecting the most accurate data possible also poses plenty of challenges.

Bias in research is a serious issue—but the first step to addressing the most common types of research bias is to educate yourself about them. From there, improving your research design becomes easier—especially when you use high-quality research tools like Dovetail.

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