What are robust statistical methods and why are they important?

What are robust statistical methods and why are they important?

Robust statistical methods are techniques that provide reliable results even when assumptions about the data are violated. They are important because they help in making valid inferences from data that may contain outliers or be non-normally distributed.

Can you give me some examples of robust statistical methods?

Sure! Some examples include the median, trimmed means, and robust regression techniques like Huber regression.

How do these methods handle outliers?

Robust methods reduce the influence of outliers by using statistics that are less affected by extreme values, ensuring more accurate results.

When should I use robust methods instead of traditional methods?

You should consider using robust methods when your data contains outliers, is skewed, or does not meet the assumptions of traditional methods.