This review article discusses anthropometric measurement error and its impact on the assessment of nutritional status. Anthropometry involves the external measurement of human morphological traits and is widely used in nutritional assessment. However, measurement error can significantly influence both the measurement and interpretation of nutritional status. The article reviews different types of anthropometric measurement error, including unreliability (imprecision and undependability) and inaccuracy (bias, validity, accuracy). It evaluates methods for estimating measurement error, such as technical error of measurement (TEM), coefficient of reliability (R), and intraclass correlation coefficient (ICC). The article emphasizes the importance of minimizing measurement error and provides guidelines for acceptable error levels, such as R > 0.95. It also discusses how measurement error can be used to improve the interpretation of anthropometric data. The article highlights that weight and height are the most precise anthropometric measures, while skinfolds and circumferences are more prone to error. It also addresses the challenges of measurement error in different populations and the importance of proper training and standardization in anthropometry. The article concludes that while anthropometric measurement error is unavoidable, it can be minimized through careful data collection, proper training, and the use of standardized techniques. The review underscores the importance of considering measurement error when interpreting anthropometric data, especially in longitudinal studies.This review article discusses anthropometric measurement error and its impact on the assessment of nutritional status. Anthropometry involves the external measurement of human morphological traits and is widely used in nutritional assessment. However, measurement error can significantly influence both the measurement and interpretation of nutritional status. The article reviews different types of anthropometric measurement error, including unreliability (imprecision and undependability) and inaccuracy (bias, validity, accuracy). It evaluates methods for estimating measurement error, such as technical error of measurement (TEM), coefficient of reliability (R), and intraclass correlation coefficient (ICC). The article emphasizes the importance of minimizing measurement error and provides guidelines for acceptable error levels, such as R > 0.95. It also discusses how measurement error can be used to improve the interpretation of anthropometric data. The article highlights that weight and height are the most precise anthropometric measures, while skinfolds and circumferences are more prone to error. It also addresses the challenges of measurement error in different populations and the importance of proper training and standardization in anthropometry. The article concludes that while anthropometric measurement error is unavoidable, it can be minimized through careful data collection, proper training, and the use of standardized techniques. The review underscores the importance of considering measurement error when interpreting anthropometric data, especially in longitudinal studies.