Previous research shows that the diagnosis and recommendation integrated system (DRIS) and modified‐DRIS (M‐DRIS) have inherently biased data base means and CV's that affect the accuracy of nutrient diagnoses. Our objective was to remove these biases and determine their effect on phosphorus (P) and potassium (K) diagnoses for soybean (Glycine maxL.). Four sets of soybean M‐DRIS nutrient norms (linear, L; log‐transformed, LT; corrected anti‐log, CAL; and corrected anti‐log population, CALP) were derived for use in evaluating P and K deficiencies and sufficiencies. The four data bases differed in that: (i) the L, LT, and CAL norms were developed from 639 soybean leaf samples yielding in excess of 3500 kg/ha, while those for CALP were calculated from 3898 samples from all available yield levels, (ii) L norms were calculated from non‐log‐transformed nutrient data, while LT, CAL, and CALP norms were calculated from log‐transformed data, and (iii) CAL and CALP concentration means were developed by taking the anti‐log of their LT concentration means and CAL and CALP ratio means were calculated directly from their anti‐log concentration means. Results show that biased means and CV's for L produced inflated function and index values, which resulted in an over‐emphasis of P and K deficiencies compared with CAL. While log‐transforming data used to derive nutrient norms resulted in removal of mathematical biases from the means and CV's, use of LT norms also over‐ emphasized P and K deficiencies compared with CAL. Phosphorus and K diagnoses by CAL and CALP did not differ, indicating that leaf analyses from all appropriate plants may be used to derive corrected anti‐log norms without regard to yield level.