Alternatives to some statistical conventions

As I have developed my ability to read the epidemiological literature and explain the methods and controversies over methods to others, I have taken note of approaches or perspectives that depart from statistical conventions. The attached file includes some items from my mixed grab bag of alternatives. There is no grand theory linking them. Readers might have objections to some of the alternatives and the thinking behind them, but they might also be stimulated to explore their implications further. It does not matter if you end up sticking with the conventional approaches; the alternatives are offered here in the spirit of critical thinking, namely, that we understand ideas better by holding them in tension with alternatives.
Question to readers: What conventions of statistical practice frustrate you and what alternatives have you considered?

AlternativesToSomeStatisticalConventions

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About Peter J. Taylor

Peter Taylor teaches and directs programs on critical thinking, reflective practice, and science-in-society at the University of Massachusetts Boston. He studies the complexity of environmental and health sciences in their social context as well as innovation in teaching, group process, and interdisciplinary collaboration (see bit.ly/pjtaylor). He is especially interested in conversations with others who are, in diverse ways, "troubled by heterogeneity" (bit.ly/tbhblog)
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