Likelihood functions, as well as the more recent concept of evidential likelihood, are essential statistical tools to perform estimation. Beyond the maximal likelihood value, the shape of the likelihood can also give interesting information about the data used to get the estimation. Indeed, it is generally acknowledged that more uncertain and scarce data will lead to flatter likelihoods. However, different aspects can affect this shape, and it may be worthwhile to separate various influences. In this paper, we discuss these influences and propose some practical ways to separate them into different measures. We demonstrate our approach on the particular case of the multinomial likelihood, which plays an important role in applications such as classification.