Quantifying signal repertoire size is a critical first step towards understanding the evolution of signal complexity. However, counting signal types can be so complicated and time consuming when repertoire size is large, that this trait is often estimated rather than measured directly. We studied how three common methods for repertoire size quantification (i.e., simple enumeration, curve-fitting and capture-recapture analysis) are affected by sample size and presentation style using simulated repertoires of known sizes. As expected, estimation error decreased with increasing sample size and varied among presentation styles. More surprisingly, for all but one of the presentation styles studied, curve-fitting and capture–recapture analysis yielded errors of similar or greater magnitude than the errors researchers would make by simply assuming that the number of types in an incomplete sample is the true repertoire size. Our results also indicate that studies based on incomplete samples are likely to yield incorrect ranking of individuals and spurious correlations with other parameters regardless of the technique of choice. Finally, we argue that biological receivers face similar difficulties in quantifying repertoire size than human observers and we explore some of the biological implications of this hypothesis.