Sample Selection Bias

What is Sample Selection Bias?

Sample selection bias is the bias that results from the failure to ensure the proper randomization of a population sample. The flaws of the sample selection process lead to situations where some groups or individuals in the population are less likely to be included in the sample.

The presence of sample selection bias may distort the statistical analysis of a sample and affect the statistical significance of the chosen statistical tests. In addition, the statistical parameter may be overstated or understated and not representative of the entire population.

Although survivorship bias is commonly considered separately, it is a special type of the sample selection bias.

Types of Sample Selection Bias

Sample selection bias may take different forms. The most common types of sample selection bias include the following:

1. Self-selection

Self-selection happens when the participants of the study exercise control over the decision to participate in the study to a certain extent. Since the participants may decide whether to participate in the research or not, the selected sample does not represent the entire population.

2, Selection from a specific area

The participants of the study are selected from certain areas only while other areas are not represented in the sample.

3. Exclusion

Some groups in the population are excluded from the study.

4. Survivorship bias

Survivorship bias ccurs when a sample is concentrated on subjects that passed the selection process and ignores subjects that did not pass the selection process. The survivorship bias results in overly optimistic findings from the study.

5. Pre-screening of participants

The participants of the study are recruited only from particular groups. Thus, the sample will not represent the entire population of the study.

How to Overcome Bias

Since sample selection bias may significantly distort the results of the study and lead to erroneous conclusions, a researcher should know how to deal with this type of bias.

The most obvious method is the establishment of a random sample selection process. By analyzing the population of the study and by identifying the subgroups of the population, a researcher must ensure that the selected sample represents the total population as much as possible.

However, if some of the population subgroups in the selected sample are underrepresented while other groups are overrepresented, a researcher may apply a statistical correction. The misrepresented groups may be assigned weights that will correct the bias.

Related Readings

Thank you for reading CFI’s guide on Sample Selection Bias. To keep learning and developing your knowledge of financial analysis, we highly recommend the additional CFI resources below:

• Data-Mining Bias
• Framing Bias
• Hypothesis Testing
• Random Sampling
• Total Probability Rule
• See all data science resources