Bias refers to errors or changes in how data is collected, analyzed, interpreted or reviewed that result in incorrect conclusions. Bias can occur at any phase of a study, from its initial design to the final analysis and reporting of results. Identifying and preventing bias is important because it affects the validity and reliability of research findings.
When researchers say a study has a high risk of bias, they mean its design or conduct makes the results less reliable. This doesn’t necessarily mean the researchers made mistakes or acted dishonestly.
Common types of bias include:
- Selection bias: The people included in the study don’t represent the wider population.
- Example: A study only includes younger IVF patients with a good prognosis.
- Confounding: Another factor influences the outcome, making it difficult to know whether the treatment caused the result.
- Example: Women taking a supplement are also younger on average, so age, and not the supplement, may explain the better outcomes.
- Information bias: Incorrect or incomplete information is collected.
- Example: Participants don’t accurately remember how much alcohol they drank before treatment.
- Attrition bias: More participants drop out of one group than the other.
- Example: Women with poor outcomes are more likely to leave the study, making the results appear better.
- Reporting bias: Researchers report only some of the outcomes they measured.
- Example: A study highlights pregnancy rates but doesn’t report live birth rates.
- Publication bias: Studies with positive findings are more likely to be published than studies showing no difference.
- Example: Five negative studies remain unpublished while one positive study is published.
Many systematic reviews and meta-analyses, which combine the results of multiple studies, evaluate the risk of bias of the studies they include. This helps readers judge how reliable the overall evidence is, since studies with a lower risk of bias generally provide more trustworthy results.