Understanding confirmation bias in research

Confirmation bias is the tendency to seek out, interpret, and remember information in ways that confirm what you already believe—while overlooking evidence that contradicts it. In research, it skews everything from how you frame a hypothesis to which data you collect and how you report your findings.

It’s also one of the hardest biases to recognize in yourself. We’re quick to spot it in others and slow to notice it in our own thinking.

This article covers the forms confirmation bias takes, the damage it can do to research, and practical ways to keep it out of your work.

History of confirmation bias

Awareness of bias goes back as far as Aristotle and Plato. Aristotle noticed people are more likely to believe arguments that support their bias. Plato noticed the challenge of overcoming bias when seeking the truth. Neither called it “confirmation bias,” but they were certainly aware of its effects.

The first psychological evidence of confirmation bias came from an experiment by psychologist Peter Wason. Subjects were asked to guess a rule governing a sequence of numbers. Participants could test any numbers they wanted before guessing the rule—yet most only tested numbers that confirmed their initial guess.

Modern technology has let scientists identify the neural underpinning of confirmation bias. Researchers from Virginia Tech used neural imaging to confirm that the brain weighs evidence confirming a person’s bias more highly than disconfirming evidence.

Types of confirmation bias

Confirmation bias comes in many forms, and each one keeps you from getting the complete picture of a research area. Understanding how the bias presents itself can help you avoid it in your methodologies.

The biases that affect research can be grounded in beliefs formed outside the lab, so evaluate how all your preconceived notions might skew your work.

Information selection bias

Information selection bias occurs when you seek out information that supports your existing beliefs—often subconsciously. Information that makes you feel correct is more enjoyable to consume than information that challenges strongly held beliefs. It can also lead you to ignore or dismiss viewpoints that don’t align with your thinking.

Information here doesn’t just mean news sources or scientific studies. The people you spend time with are a major source of information about the world, and choosing friend groups that never challenge your beliefs can be a significant source of confirmation bias.

Social media compounds the problem. Many people carefully curate their feeds, and dissenting opinions often get treated as unfathomable evil rather than mere disagreement. The result is strong echo chambers—and an equally strong resistance to understanding people who disagree with you.

Social scientists especially need to be aware of how these biases may shape their conclusions.

Interpretation bias

Data can often be interpreted in more ways than one. With motivated reasoning, even clear data can be distorted to better align with your views. When data is misrepresented to fit a particular line of reasoning, it’s known as interpretation bias.

A common form is emphasizing data that supports a preconceived notion while downplaying data that doesn’t.

Whether it’s a study you’ve conducted or one guiding your research, it’s easy to focus on the parts that reinforce what you already believe and ignore the rest. Doing so can prevent you from finding evidence that would disprove your theory—and make it harder to solve the problem at hand.

Memory bias

Downplaying disconfirming data hurts research in the moment, but it also has knock-on effects later. Data that confirms your biases sticks in your mind, while data that doesn’t fades away.

This form of confirmation bias is called memory bias. It’s harder to catch on a particular project because you can’t be aware of something you don’t remember.

A big part of conducting research is relying on work others have done before you. A literature review can guide your research and help you form conclusions. But if you only focus on studies that confirm your suspicions and don’t put in the effort to find studies that challenge your findings, you’ll introduce .

Confirmation-seeking bias

Wason’s experiment, described earlier, is an example of confirmation-seeking bias. The subjects only tested the rule they believed to be true and didn’t properly explore the options, so they reached the wrong conclusion.

This bias shows up in poorly designed experiments, or in searching only for data and research that confirms your views. In its most extreme form, balanced or disconfirming sources are purposefully ignored or dismissed to confirm a bias instead of answering a .

Here’s an example from outside the lab, one political scientists may be particularly susceptible to. News sources increasingly serve a particular ideological bent, and many people rely only on sources that paint a one-sided political picture. We’re good at recognizing this behavior in others—much less so in ourselves.

Impact of confirmation bias

The impacts you have the most control over are those that affect you directly. They’ll weaken your results unless you’re careful to recognize and avoid your biases.

Common impacts of confirmation bias:

Biased hypotheses: confirmation bias can lead you to form a hypothesis based more on existing beliefs than meaningful data, biasing the project from the start.
Data collection and interpretation: during the phase, you may unconsciously focus on data that supports your hypotheses, distorting the findings.
Selective reporting: in more extreme cases, you may choose to report only the findings that confirm your beliefs.
Misinterpretation of results: you may incorrectly interpret ambiguous or inconclusive findings you’d otherwise treat with more caution.
Poor study design: you may unintentionally design experiments where results are more likely to confirm a hypothesis instead of seeking a more balanced design.

Some impacts of confirmation bias affect the scientific community more broadly. When a field is dominated by a particular ideology or belief system, several negative consequences can follow:

Publication bias: studies that align with prevailing views may be more likely to get published than those that push against them, regardless of the strength of the research.
Peer review and feedback: both sides of peer review can suffer. Reviewers may dismiss studies they disagree with or go easy on ones they like. Authors may reject valid criticism that challenges their beliefs.
Replication issues: the best proof of a study’s validity is someone else replicating it. If confirmation bias shaped the results, researchers without that bias may struggle to replicate them—contributing to the replication crisis some fields have experienced.

Signs of confirmation bias

Knowing the signs of confirmation bias helps you recognize it in yourself and work past it. The bias is complex, as the number of forms it takes shows.

Ignoring contradictory evidence

It isn’t uncommon for people to ignore evidence that contradicts their preconceived notions. Everyone does it to some extent, so it pays to know what to watch for.

Common signs include:

Selectively focusing on data that supports your position while neglecting conflicting data
Deliberately avoiding situations that might expose you to opposing viewpoints
Suppressing or dismissing evidence that causes discomfort due to conflicting beliefs

Selective exposure to information

It’s easiest to ignore disconfirming evidence if you never see it in the first place. Selective exposure is a major problem for anyone who wants both sides of the picture and conclusions based on fact rather than bias.

Signs you’re falling into selective exposure:

Actively seeking out sources that confirm your existing beliefs
Unconsciously avoiding information that challenges your worldview
Preferring news outlets and websites that align with your personal opinions

Over-relying on anecdotal evidence

There’s a joke that the plural of anecdote isn’t “anecdata.” Yet many people treat anecdotal evidence as more concrete than hard data when the anecdotes fit their preferred narrative. Ways you may catch yourself in this trap:

Giving more weight to personal stories or experiences than concrete data
Being swayed by emotionally charged stories that resonate with your current beliefs
Drawing conclusions from individual experiences to make broader claims

Misinterpreting ambiguous information

The human brain has a habit of filling in gaps, and ambiguous information leaves plenty of gaps to fill. Almost always, the mind fills them with information that supports an existing belief system.

Signs of this include:

Assigning meaning to ambiguous information that confirms your preexisting beliefs
Interpreting ambiguous external stimuli in a way that aligns with your existing notions
Incorrectly attributing motives or intentions to ambiguous actions to fit your assumptions

Group polarization and echo chambers

When you spend most of your time around people who agree with you, you limit the alternative perspectives you’re exposed to. Universal agreement in your circle creates a potentially false perception that the broader population shares your opinion.

These signs may indicate a lack of diversity in your relationships:

In group settings, the people you spend time with reinforce each other’s beliefs more often than not
You spend time in online and offline communities that all share the same views on a subject
The people you spend time with tend to vilify those with different opinions

How to avoid confirmation bias in research

The purpose of research is to find the truth or solve a problem. Neither happens if you’re merely reinforcing your own, possibly false, beliefs.

Beyond recognizing the signs above, here are proactive measures to make your results more sound:

Acknowledge personal biases: first, understand which way you want the research to go. Then you can design experiments that test your idea rather than simply confirm it.
Actively seek diverse perspectives: intellectual diversity is a powerful way to fight confirmation bias. The bias itself may push you away from those with differing beliefs, but taking them into account is the best way to shape your own.
Engage with contradictory information: seek out information that disconfirms your hypothesis. What arguments and data weigh against it? Accounting for them lets you better test which theories hold up.
Use and skepticism: treat findings that confirm your suspicions with the same scrutiny you’d apply to those that disconfirm them.
Employ rigorous : strict protocols and robust of the data, where applicable, help counteract the bias you bring to the research.
Peer review: just as you sought diverse perspectives when designing the research, have a trusted neutral party review your work for signs of bias.
Commit to continuous learning: as the Virginia Tech researchers showed, confirmation bias is part of how our brain works. Removing it takes ongoing effort to identify and mitigate.