What is Selection Bias?
Selection bias occurs when researchers make decisions that cause a sample to be systematically different from the population of interest.
Selection bias can arise from various decisions, such as:
- Using an improper sampling method.
- Making particular methodology and data choices.
- Choosing a study design that affects the continued participation of subjects.
All these factors can create a sample that does not represent the target population. Regardless of the cause, selection bias can seriously threaten the validity and generalizability of study findings. It can produce incorrect estimates of treatment effects and misidentify risk factors for a condition.
In short, selection bias creates a non-representative sample that can produce inaccurate or misleading findings. It is particularly problematic when people use the data to make decisions that have significant consequences, such as in medical studies or policymaking.
Learn more about Experimental Designs and Representative Samples.
Common Types of Selection Bias
Many types of selection bias can occur in research studies. Each kind has unique solutions. Consequently, you must understand the different sources to implement the correct approaches to minimize them. Let’s look at some examples.
Sampling bias is frequently caused by using a non-random sampling method, making some members of the population less likely to be included than others. This type of selection bias leads to a sample where the participants do not reflect the properties of an entire population.
With this type of bias, the sample is biased from the beginning. In the other bias examples that follow, the sample might start unbiased but become biased as the study progresses.
The best solution for sampling bias is to use random sampling from a complete list of the population.
Learn more about Sampling Bias and Sampling Methods in Research.
Time interval bias occurs when the timing of the study or data collection affects the results. This type of selection bias relates to how the researchers choose when to start and end the investigation. For example, the researchers might end the study early when the results fit the desired findings. They cherry pick an endpoint because it produces statistically significant results.
In some cases, researchers might terminate the study early for ethical reasons, especially when they observe a significant effect in the treatment group. But the bias is the same. The observed effect is a byproduct of choosing to end the study upon observing favorable results. These findings potentially reflect transient effects occurring by chance rather than long-term treatment effects.
Ideally, researchers define the experiment’s starting and endpoints in advance and explain their reasons.
Attrition bias occurs when participants drop out or fail to complete a study. With this type of selection bias, the sample can start out representative, but then dropouts cause it to become biased. This problem worsens when there is a systematic difference between those who complete the study versus those who do not. Clearly, the findings will only include those who complete the study and not reflect the differing characteristics of those who dropped out, leading to biased results.
For instance, in a study on the effectiveness of a new drug, if participants who experience adverse side effects drop out, the results may show a more favorable outcome than if those participants had remained in the study. This situation can lead to overestimating the drug’s effectiveness and potentially dangerous consequences if the drug is later approved based on these biased results.
Tracking people who drop out of the study and obtaining at least some information from them can help reduce attrition bias. This problem is similar to nonresponse bias.
Study bias occurs when there are flaws or errors in the design or implementation of a study, leading to inaccurate or biased results. This type of selection bias can occur at any stage of the study, including the methods, measurement of variables, analysis of data, and interpretation of results. Study bias can produce false conclusions that can have significant consequences, such as the approval of ineffective or even harmful treatments or therapies.
For instance, imagine a study on the effectiveness of a new drug for treating a condition. Suppose the researchers only report positive outcomes of the drug, such as decreased symptoms, while ignoring adverse side effects. In that case, the results may be misleading and lead to a false sense of safety, potentially causing harm to those who take it.
Exposure bias occurs when participants in a study are not exposed to the factors being studied in the same way as the overall population. Consequently, the relationship between exposure and the health outcome differs for people who complete a study than for those in the target population.
This type of selection bias can affect the exposure effect estimate for the outcome of interest. It can arise due to various factors. They include differences in the duration, frequency, or intensity of exposure. Or differences in the timing or measurement of exposure between study participants.
For example, imagine a study on the effectiveness of a new drug for treating a particular disease. If the researchers include only patients with a mild form of the disease and exclude those with more severe symptoms, the results may overestimate the drug’s effectiveness. Mild cases may respond better to the treatment, making it appear more effective than it is for the broader population with the disease.
Understanding exposure issues and carefully including participants with exposures representing the whole population can reduce this form of selection bias.
Data bias occurs when the researchers decide to include, exclude, or analyze data in a manner that biases the results. There are countless ways this type of selection bias can occur. Using statistical analysis to draw proper conclusions requires making many correct decisions. One wrong choice can bias the results!
Here are two examples of data bias out of numerous possibilities:
- Post hoc data alteration refers to changing which data the researchers include in the analysis after the fact based on subjective or arbitrary reasons. This approach can bias the results if they choose specific data subsets to support a preconceived conclusion.
- Researchers can reject data for arbitrary reasons rather than following agreed-upon criteria for excluding data. Additionally, data points that statistical methods flag as outliers and discarded purely on those grounds may actually contain important information that is lost by ignoring them.
Ideally, the researchers describe their data procedures before conducting the study to help prevent themselves from cherry-picking approaches after collecting the data.
Selection bias is a critical issue that can affect the accuracy and reliability of research findings. Understanding the different types of selection bias can help researchers identify potential sources of bias and take steps to mitigate them.
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