This article provides a guide to correlation coefficients, explaining their use, interpretation, and naming conventions in medical research. Correlation measures the strength and direction of a relationship between two variables, denoted by 'r', which ranges from -1 to +1. A value of 0 indicates no correlation, while 1 indicates a perfect positive correlation. Negative values indicate inverse relationships. It is important to report both the strength and direction of the correlation when writing a manuscript, as the same strength of r may be named differently by different researchers. The article discusses various types of correlation coefficients, including Pearson's r, Spearman's rho, Kendall's tau, Phi, Cramer's V, and Lin's concordance correlation coefficient (CCC). Pearson's r is used for linear relationships between continuous variables, while Spearman's rho and Kendall's tau are used for non-parametric data. Phi and Cramer's V are used for categorical data. Lin's CCC measures both precision and accuracy in bivariate data. The strength of a correlation is often misinterpreted, with statistically significant results not necessarily indicating a strong relationship. The article emphasizes the importance of correctly interpreting the strength of correlations and avoiding overinterpretation. Different sources provide varying interpretations of the strength of correlation coefficients, and authors should be aware of these differences when reporting their findings.This article provides a guide to correlation coefficients, explaining their use, interpretation, and naming conventions in medical research. Correlation measures the strength and direction of a relationship between two variables, denoted by 'r', which ranges from -1 to +1. A value of 0 indicates no correlation, while 1 indicates a perfect positive correlation. Negative values indicate inverse relationships. It is important to report both the strength and direction of the correlation when writing a manuscript, as the same strength of r may be named differently by different researchers. The article discusses various types of correlation coefficients, including Pearson's r, Spearman's rho, Kendall's tau, Phi, Cramer's V, and Lin's concordance correlation coefficient (CCC). Pearson's r is used for linear relationships between continuous variables, while Spearman's rho and Kendall's tau are used for non-parametric data. Phi and Cramer's V are used for categorical data. Lin's CCC measures both precision and accuracy in bivariate data. The strength of a correlation is often misinterpreted, with statistically significant results not necessarily indicating a strong relationship. The article emphasizes the importance of correctly interpreting the strength of correlations and avoiding overinterpretation. Different sources provide varying interpretations of the strength of correlation coefficients, and authors should be aware of these differences when reporting their findings.