Detecting outliers in data is important for understanding unusual points that don't fit with the rest of the information. In multivariate data, outliers can be defined in different ways and don't follow a specific statistical model. Different methods have been developed to detect these outliers, each focusing on a specific type of outlier. After applying these methods to two sets of data, it was found that no single approach is better than the others, as they all aim to identify outliers in their own unique way.