  Comparing alternatives in pairs is a very well known technique of ranking
creation. The answer to how reliable and trustworthy ranking is depends on the
inconsistency of the data from which it was created. There are many indices
used for determining the level of inconsistency among compared alternatives.
Unfortunately, most of them assume that the set of comparisons is complete,
i.e. every single alternative is compared to each other. This is not true and
the ranking must sometimes be made based on incomplete data. In order to fill
this gap, this work aims to adapt the selected twelve existing inconsistency
indices for the purpose of analyzing incomplete data sets. The modified indices
are subjected to Monte Carlo experiments. Those of them that achieved the best
results in the experiments carried out are recommended for use in practice.
