What are robust statistics and why are they important?

What are robust statistics and why are they important?

Robust statistics are methods 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 an example of a robust statistical method?

Sure! One common example is the median, which is less affected by outliers compared to the mean. Another example is the use of trimmed means.

How do robust statistics differ from traditional statistics?

Robust statistics focus on providing accurate results under a wider range of conditions, while traditional statistics often rely on strict assumptions about the data, such as normality.

When should I use robust statistics?

You should use robust statistics when your data may contain outliers, is skewed, or does not meet the assumptions required for traditional statistical methods.