QCA can be performed probabilistically or deterministically with observations of categorical variables. For instance, the existence of a descriptive inference or implication is supported deterministically by the absence of any counter-example cases to the inference; . if a researcher claims condition X implies condition Y, then, deterministically, there must not exist any counterexample cases having condition X, but not condition Y. However, if the researcher wants to claim that condition X is a probabilistic 'predictor' of condition Y, in another similar set of cases, then the proportion of counterexample cases to an inference to the proportion of cases having that same combination of conditions can be set at a threshold value of for example 80% or higher. For each prime implicant that QCA outputs via its logical inference reduction process, the "coverage" — percentage out of all observations that exhibit that implication or inference — and the "consistency" — the percentage of observations conforming to that combination of variables having that particular value of the dependent variable or outcome — are calculated and reported, and can be used as indicators of the strength of such a explorative probabilistic inference. In real-life complex societal processes, QCA enables the identification of multiple sets of conditions that are consistently associated with a particular output value in order to explore for causal predictors.