This article provides a guide to understanding correlation coefficients in medical research. It highlights the importance of clearly reporting the strength and direction of correlation coefficients, as different researchers may interpret the same correlation coefficient differently. 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). Each coefficient has specific use cases and interpretations. For example, Pearson's r is used for normally distributed continuous variables, while Spearman's rho and Kendall's Tau are used for non-normal data. The article emphasizes that correlation does not imply causation and that statistical significance does not necessarily indicate a strong relationship. It also provides tables summarizing the interpretations of different correlation coefficients. The conclusion stresses the need for authors to avoid overinterpreting the strength of associations when reporting correlation coefficients in their manuscripts.This article provides a guide to understanding correlation coefficients in medical research. It highlights the importance of clearly reporting the strength and direction of correlation coefficients, as different researchers may interpret the same correlation coefficient differently. 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). Each coefficient has specific use cases and interpretations. For example, Pearson's r is used for normally distributed continuous variables, while Spearman's rho and Kendall's Tau are used for non-normal data. The article emphasizes that correlation does not imply causation and that statistical significance does not necessarily indicate a strong relationship. It also provides tables summarizing the interpretations of different correlation coefficients. The conclusion stresses the need for authors to avoid overinterpreting the strength of associations when reporting correlation coefficients in their manuscripts.