Which method is commonly used to reduce bias in research?

Using a larger sample size is a widely recognized method for reducing bias in research. When researchers increase the number of participants in a study, it generally leads to a more representative sample of the population, thus minimizing the impact of outliers or anomalies present in smaller groups. A larger sample size enhances the statistical power of the research, making it easier to detect actual effects or relationships in the data while reducing the likelihood that the findings are due to random chance. This contributes to the overall validity and reliability of the study results, allowing the conclusions drawn to be more applicable to the larger population being studied.

In contrast, eliminating all dependent variables is not practical, as dependent variables are essential for measuring the effects of independent variables in any research design. Conducting interviews may provide rich qualitative data, but it can also introduce bias based on the interviewer's framing and interaction with participants. Introducing subjective analysis can similarly lead to bias, as it relies on personal interpretations that can skew the results away from an objective standpoint. Therefore, increasing sample size remains the most effective and straightforward method for minimizing bias in research.