Distance measures are mathematical tools used to quantify the similarity or dissimilarity between two points in a given space. Commonly used in fields like statistics, machine learning, and data analysis, these measures help in clustering, classification, and other analytical tasks. Examples include Euclidean distance, which calculates the straight-line distance between two points, and Manhattan distance, which sums the absolute differences of their coordinates. Different distance measures can yield different insights depending on the context. For instance, Cosine similarity is often used in text analysis to determine how similar two documents are, while Hamming distance is useful in coding theory to measure the difference between two strings of equal length. Choosing the right distance measure is crucial for accurate analysis.