The friction factor is a crucial parameter in calculating frictional pressure losses. However, it is a decisive challenge to estimate, especially for turbulent flow of non-Newtonian fluids in pipes. The objective of this paper is to examine the validity of friction factor correlations adopting a new informative-based approach, the Akaike information criterion(AIC) along with the coefficient of determination(R2). Over a wide range of measured data, the results show that each model is accurate when it is examined against a specific dataset while the El-Emam et al.(Oil Gas J 101:74–83, 2003) model proves its superiority. In addition to its simple and explicit form, it covers a wide range of flow behavior indices and generalized Reynolds numbers. It is also shown that the traditional belief that a high R2 means a better model may be misleading. AIC overcomes the shortcomings of R2 as a trade between the complexity of the model and its accuracy not only to find a best approximating model but also to develop statistical inference based on the data. The authors present AIC to initiate an innovative strategy to help alleviate several challenges faced by the professionals in the oil and gas industry. Finally, a detailed discussion and models’ ranking according to AIC and R2 is presented showing the numerous advantages of AIC. The friction factor is a crucial parameter in calculating frictional pressure losses. However, it is a decisive challenge to estimate, especially for turbulent flow of non-Newtonian fluids in pipes. The objective of this paper is to examine the validity of friction factor correlations. a new informative-based approach, the Akaike information criterion (AIC) along with the coefficient of determination (R2). Over a wide range of measured data, the results show that each model is accurate when it is examined against a specific dataset while the El-Emam et al. (Oil Gas J 101: 74-83, 2003) model proves its superiority. In addition to its simple and explicit form, it covers a wide range of flow behavior indices and generalized Reynolds numbers. It is also shown that the traditional belief that a high R2 means a better model may be misleading. AIC overcomes the shortcomings of R2 as a trade between the complexity of the model and its accuracy not only to find a best approximating model but also to develop statistical inference based on the data. The authors present AIC to aid alleviate several challenges faced by the professionals in the oil and gas industry. Finally, a detailed discussion and models’ ranking according to AIC and R2 is presented showing the numerous advantages of AIC.