 What is considered inference (anumāna) relies on data provided by perception which makes inference doubtful. But inference the way it has so far been defined is flawed for a number of other reasons. There is no reliable way to relate properties or facts in a truth-conducive way. For instance, to infer the cause from the effect one would have to first define what causality is, which as Jayarāśi demonstrates cannot be done with the categories we have at our disposal: there is actually no reliable way to relate A and B as cause and effect. Further, what do we relate in inference: universals with universals, universals with a particular, or a particular with a particular? Since universals do not exist, inferences which are based on such notions are intrinsically flawed. We are left with the idea that inferences depend on particular-to-particular relation. That being the case, there is no method to establish any kind of valid relationship of universal  character between two particulars which could allow us to draw any sound inferences from single-instantiated cases. Further, inferences will have to rely on cases of inductive reasoning which are logically not valid and cannot be demonstrated to be universally valid. Also testimony of an authority cannot be a legitimate source of knowledge because, first, such testimony would have to rely on other cognitive criteria and, second, we would have no means to determine what a reliable testimony is or who an authoritative person can be. Therefore, given all available definitions and criteria admitted by the tradition, nothing can be known for certain (Balcerowicz (2019: 918–­924)).