ABSTRACT
It is interesting to see that many famous statisticians were involved in the discussion of randomness in the years 1985, 1986, and 1987. In statistical inference, if a phenomenon is not random, a man-made randomization can be induced to achieve the randomness needed in a legitimate calculation. Scientific thinking includes deductive and inductive reasoning. Deductive reasoning is epitomized by mathematics and logic; the latter deals mainly with validity of arguments rather than the truth of our universe. Statistical inference, which generalizes results from a sample to a population, is as confusing and scandalous as induction. The summary statistics drawn from a grab set are of great value for scientific endeavors, but they are descriptive, not inferential statistics. To experienced data analysts, the characteristics of real-life data usually are like this: non-normal, non-random, heavy prior, and small sample. The demarcation between genuine- and quasi-inferential statistics is a clearly defined and well-justified chance model.
