In project quality management, which statistical method is used to identify which factors may influence specific variables of a product or process under development or in production?

Introduction / Context:
Project Management Professional (PMP) style exams often test your understanding of quality management tools and techniques. One important tool is a statistical method that helps determine which factors influence key variables of a product or process. This method is particularly useful when designing experiments during product development or improving processes already in production. Knowing the correct name and purpose of this method is essential for quality related questions.

Given Data / Assumptions:

• The context is project quality management within the PMP framework.
• The method in question is explicitly described as a statistical method.
• It is used to identify which factors or inputs influence specific variables or outputs of a product or process.
• Answer choices include cost benefit analysis, sensitivity analysis, cost of quality analysis, and design of experiments.

Concept / Approach:
Design of experiments, often abbreviated as DOE, is a structured statistical technique for planning and analyzing controlled tests. In DOE, you systematically vary input factors to observe the effect on output variables, allowing you to identify which factors significantly influence quality or performance. Cost benefit analysis focuses on comparing costs and benefits of an option, sensitivity analysis tests how results change with variations in assumptions, and cost of quality looks at prevention, appraisal, and failure costs. Only design of experiments matches the description given in the question.

Step-by-Step Solution:
Step 1: Focus on the phrase “statistical method” and “identifying which factors may influence specific variables” in the question statement. Step 2: Recall that design of experiments is specifically intended to design and analyze controlled tests where multiple input factors are varied. Step 3: Compare this to cost benefit analysis, which is about financial evaluation, and cost of quality analysis, which categorizes cost of quality activities. Step 4: Recognize that sensitivity analysis examines the impact of changes in key assumptions but is not the primary DOE style experimentation technique, then select design of experiments.

Verification / Alternative check:
Consider a manufacturing example where you want to know whether temperature, pressure, or mixing time most affects defect rate. A design of experiments approach would vary these factors systematically and record the resulting defects, then use statistical analysis to identify significant factors and interactions. None of the other tools are built to plan and analyze such structured experiments. This practical example reinforces that DOE is the correct method.

Why Other Options Are Wrong:
Option A, cost benefit analysis, is about comparing financial costs and benefits and does not design experiments. Option B, sensitivity analysis, looks at how outputs change when inputs or assumptions vary, usually in models, but it is not the full experimental design technique described. Option C, cost of quality analysis, categorizes costs into prevention, appraisal, and failure, and is focused on economic impact of quality, not on experimental investigation of factors. Only option D matches the statistical experimentation description.

Common Pitfalls:
Test takers sometimes confuse sensitivity analysis with design of experiments because both deal with changing variables. The key difference is that DOE is a formal experimental design and analysis technique, while sensitivity analysis is often applied to models or estimates with less structured experimentation. Another mistake is assuming all quality tools are primarily financial; in reality, some are statistical, some are financial, and some are graphical. Recognizing where DOE fits within the quality toolbox is crucial for PMP style questions.

Final Answer:
The statistical method described is design of experiments.