The article shows that it's difficult to calculate certain robust estimators used in statistics for handling outliers in data. These estimators, like LMS and LTS, are important for accurately estimating parameters in regression analysis. The researchers found that some commonly used robust estimators are hard to compute, making it challenging to get accurate results when dealing with outlier data points. They also presented a dataset where a specific procedure had a very low chance of giving the correct answer. Additionally, the article discusses how to create new robust estimators to address this issue.