The paper reviews the Box-Cox transformation technique, a parametric power transformation method proposed by Box and Cox in 1964 to address issues such as non-additivity, non-normality, and heteroscedasticity in statistical analysis. The transformation aims to simplify models and improve the satisfaction of theoretical assumptions. The review covers various versions of the transformation, including modifications to handle negative observations and skew distributions, as well as estimation methods for the transformation parameter. It discusses hypothesis testing, robustness, and the handling of heteroscedasticity and autocorrelation. The paper also explores the empirical determination of functional forms, the effect of outliers, and prediction in the original scale. Despite its widespread use, the Box-Cox transformation often fails to meet all the original assumptions, but it remains valuable in empirical studies, particularly in econometrics.The paper reviews the Box-Cox transformation technique, a parametric power transformation method proposed by Box and Cox in 1964 to address issues such as non-additivity, non-normality, and heteroscedasticity in statistical analysis. The transformation aims to simplify models and improve the satisfaction of theoretical assumptions. The review covers various versions of the transformation, including modifications to handle negative observations and skew distributions, as well as estimation methods for the transformation parameter. It discusses hypothesis testing, robustness, and the handling of heteroscedasticity and autocorrelation. The paper also explores the empirical determination of functional forms, the effect of outliers, and prediction in the original scale. Despite its widespread use, the Box-Cox transformation often fails to meet all the original assumptions, but it remains valuable in empirical studies, particularly in econometrics.