What Is Recall Bias? | Definition & Examples

Published on 24 October 2022 by Kassiani Nikolopoulou. Revised on 16 March 2023.

Recall bias refers to systematic difference in the ability of participant groups to accurately recall information. Observational studies that rely on self-reporting of past behaviors or events are particularly prone to this type of bias.

Example: Recall bias

Parents whose children have developed asthma are likely to be quite concerned about what may have contributed to their child’s condition.

As a result, if asked by a researcher, these parents are more likely to recall relevant details, such as changes in their children’s breathing when active or resting, than parents of children without any health issues.

They’ve also already associated possible triggers, such as certain foods, environments, or other allergens, with their child’s asthma. This difference in the ability to recall information results in recall bias.

Recall bias threatens the internal validity and credibility of studies using self-reported data.

What is recall bias?
What causes recall bias?
Recall bias example
How to prevent recall bias
Other types of research bias
Frequently asked questions about recall bias

What is recall bias?

Recall bias is a type of research bias. It can occur whenever an attempt is made to collect data retrospectively, or after the event has already happened.

Recall bias is a common problem in research studies that rely on self-reporting, such as case-control, cross-sectional, and retrospective cohort studies. The time that elapses between the interview or survey and the phenomenon under study can influence participants’ recollections.

Example: Recall bias in case-control studies

Case-control studies are particularly susceptible to recall bias. The goal of these studies is to identify factors that may contribute to a medical condition by comparing data from a case group against a control group.However, individuals with a specific disease (the case group) have likely already gone over their life history in an attempt to understand why they became ill. Meanwhile, individuals in the unaffected control group are less likely to have done so. This increases the chances of recall bias.

As a result, the case group is more likely to assign significance to past events. They may be able to recall a greater number of potential risk factors than the control group, even if these events or factors didn’t necessarily contribute to their current condition or disease.

This can lead researchers to exaggerate the correlation between a potential risk factor and a disease.

What causes recall bias?

Recall bias is caused by an inaccurate or incomplete recollection of events by study participants. Research shows that remarkable or infrequent events, such as buying a house, are more memorable for longer periods of time than everyday events, such as driving to work.

In general, the following conditions increase the chances of recall bias in studies using self-reported data:

The disease or event under investigation is significant or critical, such as heart disease.
A participant has preconceived notions about the link between their health condition and a certain risk factor. For example, they attribute their condition to electromagnetic fields produced by nearby power lines.
A scientifically unfounded association is popularised by the media, such as claiming a link between artificial light and increased risk of breast cancer.
The study requires participants to report socially undesirable behaviors, for example substance misuse during pregnancy.
The case and control groups are different in ways that may influence their ability to recall information, such as when comparing participants with chronic pain to healthy participants.

It is important to keep in mind that recall bias is not the same as forgetfulness. If the extent of forgetfulness regarding past events is equal in the case and control groups, recall bias will not occur. If one group remembers previous events or experiences more accurately than the other, then recall bias is at play.

Recall bias example

Recall bias can cause researchers to draw false conclusions regarding the causes of an event, disease, or condition.

Example: Assigning more significance to past events

Prior to discovering that Down syndrome was caused by genetics, researchers spent a long time trying to find out what caused it.In the studies conducted on the subject, women who gave birth to children with Down syndrome reported more traumatic events during their pregnancy than mothers of children without Down syndrome. Due to that, researchers concluded that emotional distress played a role in the chance of giving birth to a baby with the syndrome.

However, later research indicated that genetic factors were the cause, disproving the initial conclusions.

Often, the experience of being studied encourages individuals to search through their memories in an effort to explain what happened. In doing so, they may assign more significance to certain events. This often leads to recall bias.

Here, women who had given birth to children with Down syndrome displayed a tendency to over-report ordinary events as traumatic. As a result, researchers wrongly concluded that there was an association between emotional distress during pregnancy and Down syndrome. Recall bias had misled researchers.

How to prevent recall bias

If your research involves asking participants to self-report, there are steps you can take to prevent or minimise recall bias:

Run a pilot for your survey. Conduct focus groups or similar to find out what a reasonable recall period for the event, experience, or behavior under study is. If possible, test out shorter and longer periods, checking for differences in recall.
Reduce the time interval between the event under study and its assessment. For example, add follow-up surveys or personal journals to reduce recall periods.
Whenever possible, use objective preexisting records, such as medical records, rather than relying on participant experiences.
Set up an appropriate control group, with comparable characteristics to the case group—for example, those with a different disease unrelated to the disease under study.
Use questionnaires that are carefully constructed in order to get accurate and complete information.

Other types of research bias

Cognitive bias

Selection bias

Other

Frequently asked questions about recall bias

What are the main types of information bias?: Information bias is a general term describing various forms of research bias arising due to systematic measurement error. The main types of information bias are:

Recall bias

Observer bias

Performance bias

Regression to the mean (RTM)
Why is bias in research a problem?: Bias in research affects the validity and reliability of your findings, leading to false conclusions and a misinterpretation of the truth. This can have serious implications in areas like medical research where, for example, a new form of treatment may be evaluated.
How does an observational study differ from an experiment?: The key difference between observational studies and experiments is that, done correctly, an observational study will never influence the responses or behaviours of participants. Experimental designs will have a treatment condition applied to at least a portion of participants.

Sources for this article

We strongly encourage students to use sources in their work. You can cite our article (APA Style) or take a deep dive into the articles below.

This Scribbr article

Nikolopoulou, K. (2023, March 16). What Is Recall Bias? | Definition & Examples. Scribbr. Retrieved 21 November 2024, from https://www.scribbr.co.uk/bias-in-research/what-is-recall-bias/

Cite this article

Sources

Hassan, E. (2005). Recall bias can be a threat to retrospective and prospective research designs. The Internet Journal of Epidemiology, 3(2). https://ispub.com/IJE/3/2/13060

Raphael, K. (1987). Recall bias: A proposal for assessment and control. International Journal of Epidemiology, 16(2), 167–170. https://doi.org/10.1093/ije/16.2.167

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Kassiani Nikolopoulou

Kassiani has an academic background in Communication, Bioeconomy and Circular Economy. As a former journalist she enjoys turning complex scientific information into easily accessible articles to help students. She specialises in writing about research methods and research bias.