Various techniques of fitness landscape analysis for the determination of optimisation problem hardness for evolutionary algorithms are proposed in the literature. However, a few implementations of these techniques and their application in practice are described nowadays. In the paper comparative statistical and information analysis for benchmark fitness functions such as Sphere, Rastrigin, Rosenbrock and Ackley functions is performed. Both statistical and information measures for benchmark fitness landscapes are estimated and interpreted in the optimisation context. The sensitivity analysis is performed to determine how the conditions of experimental analysis and the noise factor will impact the target measures and their estimations.