Quantifying the functional diversity in ecological communities is very promising for both studying the response of diversity to environmental gradients and the effects of diversity on ecosystem functioning (i.e. in “biodiversity experiments”). In our view, the Rao coefficient is a good candidate for an efficient functional diversity index. It is, in fact, a generalization of the Simpson’s index of diversity and it can be used with various measures of dissimilarity between species (both those based on a single trait and those based on several traits). However, when intending to quantify the functional diversity, we have to make various methodological decisions such as how many and which traits to use, how to weight them, how to combine traits that are measured at different scales and how to quantify the species’ relative abundances in a community. Here we discuss these issues with examples from real plant communities and argue that diversity within a single trait is often the most ecologically relevant information. When using indices based on many traits, we plead for careful a priori selection of ecologically relevant traits, although other options are also feasible. When combining many traits, often with different scales, methods considering the extent of species overlap in trait space can be applied for both the qualitative and quantitative traits. Another possibility proposed here is to decompose the variability of a trait in a community according to the relative effect of among- and within-species differentiation (with the latter not considered by current indices of functional diversity), in a way analogical to decomposition of Sum of squares in ANOVA. Further, we show why the functional diversity is more tightly related to species diversity (measured by Simpson index) when biomass is used as a measure of population abundance, in comparison with frequency. Finally, the general expectation is that functional diversity can be a better predictor of ecosystem functioning than the number of species or the number of functional groups. However, we demonstrate that some of the expectations might be overrated – in particular, the “sampling effect“ in biodiversity experiments is not avoided when functional diversity is used as a predictor.