Fitting of the distributions for CV value of the groundnut experiments

Abstract:

The coefficient of variation (CV) is a widely used statistical measure to assess variability in agricultural experiments due to its scale-invariant property and ease of interpretation compared to variance. It is commonly applied to compare variability across traits or populations and to evaluate experimental homogeneity; a CV within acceptable limits indicates uniformity within blocks. Determining an appropriate probability distribution for modelling CV data is a key concern in agricultural research. In this study, seven probability distributions viz., Normal, Lognormal, Gamma, Weibull, Exponential, Beta and Erlang were evaluated for their suitability in representing CV values. The goodness of fit for each distribution was assessed using four statistical tests: Kolmogorov-Smirnov, Cramer-von Mises, Anderson-Darling and Chi-square tests. These tests were applied to each dataset to identify the most appropriate distribution. Additionally, different forms of the selected distributions were examined to better understand the shape and characteristics of CV data. The study provides insights into selecting suitable probability models for CV values, facilitating improved interpretation of variability and supporting more reliable conclusions in groundnut experiments.