Selection Bias occurs when the sample used in a study is not representative of the larger population being studied. This can lead to inaccurate or misleading results.
Types:
- Volunteer bias: When participants in a study are self-selected, they may differ systematically from those who do not participate.
- Survivorship bias: Focusing on individuals or groups that have “survived” some process, leading to misleading conclusions.
- Healthy user bias: Often seen in medical studies, where participants may be healthier than the general population.
- Non-response bias: When individuals who do not respond to a survey or study differ systematically from those who do.
Consequences:
- Inaccurate results: The findings may not reflect the true situation in the population.
- Misleading conclusions: Incorrect inferences can be drawn from the data.
- Limited generalizability: The results may not be applicable to the broader population.
To mitigate Selection Bias, researchers should strive to create representative samples using random sampling techniques and carefully consider potential sources of bias in their study design.