What Is Ascertainment Bias? | Definition & Examples

Ascertainment bias occurs when some members of the target population are more likely to be included in the sample than others. Because those who are included in the sample are systematically different from the target population, the study results are biased.

Example: Ascertainment bias
Suppose you are investigating the ratio of people who identify as male or female in a certain area. You draw your sample from a housing project for elderly people. Because, statistically speaking, women tend to live longer than men, your results could be biased in favor of women, with women overrepresented in your sample.

Ascertainment bias is a form of selection bias and is related to sampling bias. In medical research, the term ascertainment bias is more common than the term sampling bias.

What is ascertainment bias?

Ascertainment bias is a form of systematic error that occurs during data collection and analysis. It occurs when sample units are drawn in such a way that those selected are not representative of the target population.

In medical research, ascertainment bias also refers to situations where the results of a clinical trial are distorted due to knowledge about which intervention each participant is receiving.

Ascertainment bias can be introduced by:

  • The person administering the intervention
  • The person receiving the intervention
  • The investigator assessing or analysing the outcomes
  • The report writer describing the trial in detail

Ascertainment bias can influence the generalisability of your results and threaten the external validity of your findings.

There are two main sources of ascertainment bias:

  1. Data collection: Ascertainment bias is an inherent problem in non-probability sampling designs like convenience samples and self-selection samples. These samples are often biased, and inferences based on them are not as trustworthy as when a random sample is used.
  1. Lack of blinding: In experimental designs, it is important that neither the researchers nor the participants know participant group assignments. For example, if a participant knows that they are receiving a placebo, they are less likely to report benefits related to the placebo effect. As a result, the comparison between the treatment and the control group will be distorted.

Ascertainment bias examples

Example: Biased data collection
In the early days of the COVID-19 pandemic, ascertainment bias played a role in the mortality rates reported.

As there were not enough testing kits at the time, the virus was being detected though individuals who had severe enough symptoms to go to the ER.

However, it is likely that there were many asymptomatic patients who were not tested. As testing kits became widely available, more asymptomatic patients were identified, and the death rate associated with the virus decreased.

In medical research, this type of ascertainment bias occurs when there is more intense surveillance or screening for an outcome, like mortality, among the exposed (serious COVID cases) than among the unexposed (asymptomatic cases).

Blinding is an important methodological feature of placebo-controlled trials to minimise research bias and maximise the validity of the research results.

Example: Lack of blinding
Suppose a researcher uses random assignment to assign clinical trial participants to the treatment (A) and control (B) groups.

The researcher then posts the participant list to a bulletin board, where anyone on the research team has access to it. Those responsible for admitting participants could see which numbers are assigned to the placebo and which ones to the active medication.

With this information in mind, they could route participants with better prognosis to the experimental group and those with poorer prognosis to the control group, or vice versa.

How to prevent ascertainment bias

In experimental studies, ascertainment bias can be reduced by ‘blinding’ everyone involved, including those who administer the intervention, those who receive it, and those concerned with assessing and reporting the results. This is called triple blinding.

More specifically, ascertainment bias can be avoided in the following ways during the data collection phase:

  • When a placebo is compared to an active treatment, the two drugs should be similar in taste, smell, and appearance. They should also be delivered using the same procedure and in the same packaging. In this way, study participants and researchers won’t realise which drug the patient is taking.
  • The person arranging the randomisation (i.e., which patient takes which drug) should have no other involvement in the study. They should not reveal to anyone else involved in the study which patient is taking which drug. This also goes for researchers involved in assessing the outcomes.

This also reduces the risk of introducing other types of bias, such as demand characteristics and confirmation bias.

Keep in mind that bias can also be introduced after data collection. To reduce ascertainment bias in this phase, make sure that:

  • Participants remain anonymous
  • The coding of the study groups is done prior to providing the data to the researchers responsible for the analysis and reporting of the results
  • The codes remain undisclosed until the process of analysis and reporting of the trial is completed

Lastly, ascertainment bias can also affect observational studies because subjects cannot be randomised. In this case, you can reduce ascertainment bias by carefully describing the inclusion and exclusion criteria used for selecting subjects or cases.

Other types of research bias

Frequently asked questions about ascertainment bias

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.

What are common types of selection bias?

Common types of selection bias are:

What’s the difference between reliability and validity?

Reliability and validity are both about how well a method measures something:

  • Reliability refers to the consistency of a measure (whether the results can be reproduced under the same conditions).
  • Validity refers to the accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

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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.