| The problem is that causation is an oxymoron. The measurement problem is the same as the problem of induction. Max Planck understood this, read his quotes on matter. No amount of correlation increases the probability of one event followed by another. Therefore, all scientific analysis is unverifiable. Knowledge of the world is completely unjustified. Not to mention our immense presumption of consistency in world phenomena when we really have no basis for asserting uniformity of nature. Read Hume on causality. "Belief in the Causal Nexus is superstition" - Wittgenstein If knowledge requires certainty, and our method of determining certainty is an infinite regress of reduction, it is like a recursive function which never returns a value. Logic is inherently a comparison operator, and if logic is the only mechanism available to us, we are trapped in a purely relational analysis and it is impossible for the mind to conceive of certainty beyond a non-reasonable emotional preordained state of knowing. A feeling so powerful it is personally indubitable. The premise of a singularity is invalid since it is inconceivable and the idea that causation is at any time inferred via comparison is fundamentally incomprehensible. If the premise is unclear then the argument is invalid. If I say that an airplane functions on fairy dust and you make an argument about propulsion and lift, and you claim that my assertion is wrong because I have not properly performed a rational reduction and that a detail I do not understand could invalidate my entire theory, well guess what neither of us is technically correct to any degree since the test for invalidity applies equally to both of our assumptions. The concept of proof is also an oxymoron. Hence, the very concept of phenomenal causation is an oxymoron. The notion of knowledge about the world is a disguise for a coping mechanism based on hope and desire. |
The only reason "science" would claim that your assertion is wrong is if your explanation doesn't agree with reality.
However, if you collect a bunch of data that you say supports your theory, but your experimental technique or data analysis is not good, then it's perfectly reasonable to point out the problems and say that your result is not supported by your procedure. This is not the same as saying that your assertion is wrong.
Another important concept that you're missing is usefulness: your assertion is unlikely to be useful (this is totally different from whether it's correct). Let's say you want to ensure that planes don't fall out of the air: how would you take advantage of your assertion to do this? (My proxy for usefulness is falsifiability -- un-falsifiable theories often are useless)