Basic rules of inference used in classical logic are Modus Ponens (MP) and Modus Tollens (MT). These two reasoning patterns start from some general knowledge about reality, expressed by true implication, "if Phi then Psi". Then basing on true premise Phi we arrive at true conclusion Psi (MP), or from negation of true conclusion Psi we get negation of true premise Phi (MT). In reasoning from data (data mining) we also use rules "if Phi then Psi'', called decision rules, to express our knowledge about reality, but in this case the meaning of the expression is different. It does not express general knowledge but refers to partial facts. Therefore decision rules are not true or false but probable (possible) only. In this paper we compare inference rules and decision rules in the context of decision networks, proposed by the author as a new approach to analyze reasoning patterns in data.