What does it mean when results are described as "robust"?

What does it mean when results are described as "robust"?

• A. The results are influenced by external factors
• B. The results are sensitive to changes in assumptions
• C. The results are consistent even with assumption violations
• D. The results can fluctuate significantly under new conditions

Answer: (C)

When results are described as "robust," it signifies that they are reliable and maintain their consistency even when there are violations of underlying assumptions. In statistical modeling, various tests and methods operate based on specific assumptions about the data (such as normality, independence, or homoscedasticity). A robust result indicates that the conclusions drawn from the analysis remain valid despite potential deviations from these assumptions. For instance, if a statistical test is robust, it will still yield reliable results even if the data contains outliers or is not perfectly normally distributed. This quality is particularly important in real-world data analysis, where perfect conditions are rarely met. Therefore, robustness speaks to the resilience of the results, making them applicable and trustworthy across different scenarios and data conditions.

When results are described as "robust," it signifies that they are reliable and maintain their consistency even when there are violations of underlying assumptions. In statistical modeling, various tests and methods operate based on specific assumptions about the data (such as normality, independence, or homoscedasticity). A robust result indicates that the conclusions drawn from the analysis remain valid despite potential deviations from these assumptions.

For instance, if a statistical test is robust, it will still yield reliable results even if the data contains outliers or is not perfectly normally distributed. This quality is particularly important in real-world data analysis, where perfect conditions are rarely met. Therefore, robustness speaks to the resilience of the results, making them applicable and trustworthy across different scenarios and data conditions.