What is the purpose of a sensitivity analysis in reliability prediction?

• A. To determine best testing schedule.
• B. To measure defect density.
• C. To identify inputs most affect predicted reliability and where improvements yield the greatest benefit.
• D. To estimate the total number of failures.

Answer: (C)

Sensitivity analysis in reliability prediction asks how changes in the inputs of a model—such as failure rates, failure modes, stress levels, or operating conditions—affect the predicted reliability. The main goal is to identify which inputs have the greatest influence on the output, so you know where improvements or better data will yield the biggest gains. This helps prioritize design changes, testing focus, and data collection to strengthen the most influential factors, making the reliability estimate more robust and the process more efficient. It also clarifies which uncertainties in the model drive the results, guiding risk management and resource allocation. For example, if reliability prediction is highly sensitive to environmental temperature, addressing temperature effects or adding margins around that variable will most effectively improve overall reliability.