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.
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 analyzing the outcomes
- The report writer describing the trial in detail
Ascertainment bias can influence the generalizability of your results and threaten the external validity of your findings.
There are two main sources of ascertainment bias:
- Data collection: Ascertainment bias is an inherent problem in non-probability sampling designs like convenience samples and self-selection samples. These samples are often at risk for biases like self-selection bias, and inferences based on them are not as trustworthy as when a random sample is used.
- 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
Blinding is an important methodological feature of placebo-controlled trials to minimize research bias and maximize the validity of the research results.
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 realize which drug the patient is taking.
- The person arranging the randomization (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 randomized. 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
- 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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