  Errors in implicative theories coming from binary data are studied. First,
two classes of errors that may affect implicative theories are singled out. Two
approaches for finding errors of these classes are proposed, both of them based
on methods of Formal Concept Analysis. The first approach uses the cardinality
minimal (canonical or Duquenne-Guigues) implication base. The construction of
such a base is computationally intractable. Using an alternative approach one
checks possible errors on the fly in polynomial time via computing closures of
subsets of attributes. Both approaches are interactive, based on questions
about the validity of certain implications. Results of computer experiments are
presented and discussed.
