Technological advances in precision fertilization are increasing the number of samples collected, decreasing the scale at which we can manage inputs in the field. Development of on-the-go sensors capable of measuring and fertilizing on scales of less than one meter placed a new challenge in precision agriculture research. Analytical tools like ANOVA and geostatistics can still be used on the high sense data analysis, however due to the complexity (high number of data in a small area) and the on-the-go sensor/fertilization applicator system these analytical tools do not provide all the information required for research. A compounding problem is that fertilizer applications can often be made at much smaller scale than yield data can be collected. The objective of this study was to determine if univariate distribution (population statistics) analysis can be useful in the study of wheat yield population response to variable rate N fertilization strategies using active NDVI sensors. The data used for this study were collected from an ongoing wheat study evaluating normalized difference vegetative index (NDVI) based on algorithms for N management. The NDVI and yield data came from four fertilization treatments. Classical analysis of variance (ANOVA) was conducted to compare treatment effect. Analysis of univariate distributions (population analysis) for NDVI and wheat yield monitor data sets was used to further evaluate the effect of the treatments. In addition to a significant effect on the mean NDVI and yield, the treatments (nitrogen algorithms) affected the normality, median, mode, skewness and kurtosis of treatment distributions. Unlike ANOVA, the analyses of univariate distributions provided an insight on how the treatments affected NDVI and yield. Understanding these dynamics will be essential for evaluation and development of prediction algorithms. Similar statistical analysis could be useful in many other agronomic evaluations.