What does a robust measure indicate about a data set?

A robust measure is one that provides a reliable indication of a data set's central tendency or variability, even when there are outliers or extreme values present. This property of robustness means that the measure is not overly influenced by unusual data points, which gives it stability across different data samples and distributions.

For instance, the median is a robust measure of central tendency because it is not affected by extremely high or low values, unlike the mean, which can be significantly skewed by outliers. This stability in the presence of outliers makes robust measures particularly valuable in statistical analysis, especially when dealing with real-world data that can often include anomalies.

In contrast, the other options mention characteristics that do not align with the definition of robust measures. Sensitivity to outliers represents the opposite of robustness, and requiring fewer data points does not directly relate to the stability of a measure. Lastly, a lack of usefulness in statistical analysis does not apply either, as robust measures can provide important insights, especially in datasets with potential outliers.