Introns comprise a considerable portion of eukaryotic genomes; however, their evolution is understudied. Numerous works of the last years largely disagree on many aspects of intron evolution. Interpretation of these differences is hindered because different algorithms and taxon sampling strategies were used. Here, we present the first attempt of a systematic evaluation of the effects of taxon sampling on popular intron evolution estimation algorithms. Using the “taxon jackknife” method, we compared the effect of taxon sampling on the behavior of intron evolution inferring algorithms. We show that taxon sampling can dramatically affect the inferences and identify conditions where algorithms are prone to systematic errors. Presence or absence of some key species is often more important than the taxon sampling size alone. Criteria of representativeness of the taxonomic sampling for reliable reconstructions are outlined. Presence of the deep-branching species with relatively high intron density is more important than sheer number of species. According to these criteria, currently available genomic databases are representative enough to provide reliable inferences of the intron evolution in animals, land plants, and fungi, but they underrepresent many groups of unicellular eukaryotes, including the well-studied Alveolata.