Extreme-value statistics is often used to estimate so-called return values (actually related to quantiles) for environmental quantities like wind speed or wave height. A basic method for estimation is the method of block maxima which consists in partitioning observations in blocks, where maxima from each block could be considered independent. Typically a block could be chosen as one year. Large portions of missing data could result in problems for estimation and seems to be an issue not highlighted in detail in the literature. The method of block maxima is here applied to real data and a related simulation study was performed, pointing out that substantially low values tend to increase the estimated return values. A plausible explanation is given by studying the redistribution of probability mass and the implications of this for the behaviours of the tails of distributions. (Contains 3 figures.)