Robust statistics refers to statistical methods that provide reliable results even when assumptions about the data are violated. Unlike traditional statistics, which can be heavily influenced by outliers or non-normal distributions, robust statistics aim to minimize the impact of these anomalies. This makes them particularly useful in real-world scenarios where data may not always fit ideal conditions. Common techniques in robust statistics include median and interquartile range, which are less affected by extreme values compared to the mean and standard deviation. By focusing on these more stable measures, robust statistics help ensure that conclusions drawn from data are valid and trustworthy, enhancing the overall quality of statistical analysis.