Statistical procedures are called robust if they remain informative and efficient in the presence of outliers and other departures from typical model assumptions on the data. Ignoring unusual observations can play havoc with standard statistical methods and can also result in losing the valuable information gotten from unusual data points. Robust procedures prevent this. And these procedures are more important than ever since currently, data are often collected without following established experimental protocols. As a result, data may not represent a single well-defined population. Analyzing these data by non-robust methods may result in biased conclusions. To perform reliable and informative inference based on such a heterogeneous data set, we need statistical methods that can fit models and identify patterns, focusing on the dominant homogeneous subset of the data without being affected by structurally different small subgroups. Robust Statistics does exactly this. Some examples of applications are finding exceptional athletes (e.g. hockey players), detecting intrusion in computer networks and constructing reliable single nucleotide polymorphism (SNP) genotyping.