Experimental power is a measure of the ability of an experiment to detect differences between treatment means. Researchers design experiments and then calculate the probability that differences are simply due to chance, the null hypothesis. The objective of the analyses reported here was to determine the appropriate number of samples to demonstrate significant differences of various magnitudes from broiler chicken blood constituents. Over 800 samples were taken for a study of the effects of sample storage time, serum vs. plasma, light intensity, and fed vs. fasted birds on blood cholesterol, triglycerides, uric acid, glucose, total protein (TP), albumin, globulin, alanine aminotransferase (ALT), aspartate aminotransferase, gammaGT, creatinine, alkaline phosphatase, Ca and P. Various transformations increased the QQ plot R2 values from 0.000 to 0.149 or 0.00 to 17.62%. Most of the QQ plot R2 values were at or above 0.90. The 1/x2 transformation of blood P data showed the biggest increase in QQ plot R2 (0.846 to 0.995). The different standard deviations and coefficients of variation (CVs) found for each variable resulted in widely different numbers of replicates needed to detect differences in 2 treatment means. The extremes were glucose with a CV of 6.9% and ALT with a CV of 39.7%. For glucose, 15 replicates are needed to find a 10% difference in 97% of experiments; for ALT, 15 replicates would detect a 50% difference 91% of the time. The use of parameters such as cholesterol, glucose, TP, albumin, and globulin showed low CVs, indicating they may be considered as stable parameters. The lower CVs make it possible to find differences with a smaller number of replicates used in studies. As reported, the phosphorus values did not have a normal distribution of the data, so a transformation of these data could be an alternative to better discuss the results found.