Inductive reasoning can be quickly summarized as a method through which a conclusion is drawn from particular cases; this conclusion may be applied to another specific case or generalized. All of our conclusions about the world around us, which we rely on daily without question, are dependent on this process. The expectation that our house will not cave in, that water will come from the faucet when turned on, that we will wake the next morning, are all propositions extrapolated from inductive arguments.
Hume in his work ‘An Enquiry Concerning Human Understanding’, after challenging the possibility of knowledge of cause and effect, posits that “The conclusions we draw from … experience are not based on reasoning or on any process of the understanding”. If it is indeed true that there is no rational basis for our acceptance of inductive reasoning, there is also no objective way to assess its validity. How do we gauge which inferences are acceptable and which are not? If it is completely arbitrary, why do we instinctively reject certain inferences as faulty?
Perhaps the greatest endeavor that owes itself to induction is science. Its claim to be in the pursuit of truth, of empirical knowledge, is entirely dependent on the validity of inductive reasoning. As such, science has developed ways and means to guarantee the validity of its conclusions; this includes randomizing samples, choosing appropriately sized sample groups and the use of statistics to calculate whether something is merely possible or is probable. Each of these methods (and there may be more) needs to be examined.
If we consider appropriately sized sample groups, we must ask ourselves how we define appropriate. If it is a particular ratio, that ratio would have to be applied universally; this is clearly not the case as we make propositions concerning the nature of atoms while rejecting others. We cannot claim that revealing extant laws of nature require less corroboration as that would require the assumption that nature is universal, an inference in itself.
If we look at the idea of randomization we run into the same problem. How random is random enough and what dictates why that which suffices in one instance is not enough for another? There must be some universality underlying the calculation which itself is an inference. Statistics are dependent upon and therefore constrained by the abovementioned elements returning us to our original question, why do we accept certain inferences and not others?
The biggest variable in any experiment is the human element; this is most evident in sociology, psychology, anthropology, and other fields of scientific inquiry that attempt to analyze our species. While in most observations the variable has limited possibilities, the complexity of human behavior means we can never guarantee true consistency nor eliminate all extraneous interferences. No matter how many safeguards are put in place to prevent error including double blinding and peer...