Six Sigma Black Belt Certified Practice Exam

What statistical method can a black belt use to assess the significance of factors in an experiment structure?

1. Analysis of variance (ANOVA)

2. Fault tree analysis (FTA)

3. Failure mode and effects analysis (FMEA)

4. Evolutionary operation (EVOP)

The correct answer is: Analysis of variance (ANOVA)

The correct answer is analysis of variance (ANOVA), which is a powerful statistical technique used to determine whether there are statistically significant differences between the means of three or more independent groups. In the context of an experimental design, black belts apply ANOVA to assess the impact of different factors on a response variable. This method helps identify which factors contribute to variability in the data and whether the differences observed are due to random chance or a true effect of the factors being tested. ANOVA allows for the comparison of multiple group means simultaneously, making it more efficient than running multiple t-tests. This capability is particularly valuable in Six Sigma projects, where understanding the significance of various factors can drive decision-making and guide process improvements. The other methods listed are not designed for assessing the significance of factors in experimental setups. Fault tree analysis focuses on identifying the causes of system failures and is typically used for reliability engineering. Failure mode and effects analysis evaluates potential failure modes within a system and their impacts, facilitating risk management rather than testing significance. Evolutionary operation is aimed at improving processes through incremental changes rather than statistical analysis of factor significance. Thus, ANOVA stands out as the appropriate choice for this context.