What are the best practices in sensitivity analysis?

What are the best practices in sensitivity analysis?

Best practices in sensitivity analysis include defining clear objectives, using appropriate models, and ensuring robust data.

Can you explain why defining clear objectives is important?

Defining clear objectives helps focus the analysis on relevant variables and ensures that the results are actionable.

What models are typically used in sensitivity analysis?

Common models include linear regression, Monte Carlo simulations, and scenario analysis, each serving different purposes.

How do I ensure my data is robust for sensitivity analysis?

To ensure robust data, validate your data sources, check for consistency, and consider the impact of outliers.