Spearman Correlation
Spearman correlation is a non-parametric statistical measure that assesses the strength and direction of monotonic relationships between two continuous or ordinal variables. It ranks the data points and calculates the correlation based on these ranks, making it robust to outliers and non-linear but monotonic patterns. It's widely used in data analysis, machine learning, and research to evaluate associations without assuming linearity or normal distribution.
Developers should learn Spearman correlation when working with data that may not meet the assumptions of Pearson correlation, such as non-normal distributions, ordinal data, or when the relationship is monotonic but not strictly linear. It's essential in fields like data science, bioinformatics, and social sciences for feature selection, hypothesis testing, and exploratory data analysis to identify trends in ranked or skewed datasets.