Ace the ACRP Clinical Research Pro Exam 2025 – Dash into Data with Confidence!

All of the options presented play a significant role in minimizing bias in clinical trials.

Using a larger sample size can enhance the reliability of the results, making it easier to detect true effects and reducing the impact of random variability. While this does not directly eliminate bias, it contributes to the overall robustness of the findings, leading to more generalizable conclusions.

Blinded assessment of outcomes is crucial for preventing bias, particularly in how outcomes are measured and interpreted. When those assessing outcomes are unaware of which participants received which treatment, it minimizes the risk of consciously or unconsciously influencing results based on knowledge of treatment assignments.

Randomization of treatment groups is a foundational aspect of clinical trial design. This method randomly assigns participants to different treatment groups, which helps to ensure that these groups are comparable at baseline. As a result, any differences in outcomes can be more confidently attributed to the treatment itself rather than to pre-existing differences among participants.

Each of these strategies addresses different types of bias—such as selection bias, measurement bias, and confounding—making their combined implementation vital for the integrity of clinical research. Thus, choosing all of the mentioned options supports a comprehensive approach to reducing bias in clinical trials.