Quote: "no testimony is sufficient to establish a miracle, unless the testimony be of
such kind, that its falsehood would be more miraculous,
than the fact which it endeavours to establish." David Hume.
Reading: Why Most Published Research Findings
Are False, John P. A. Ioannidis PLoS Med 2(8): e124. DOI: 10.1371/journal.pmed.0020124
Working on: Crowding: large molecules are in fact WORSE at crowding than small ones like water!
Translation of Boltzmann paper now published on line in Entropy (2015) 17:1971
Paper: Calculation of entropy from data of motion, S-K Ma J. Stat. Phys. 26:221 (1981). A classic.
Book: Harold Jeffreys "Scientific Inference" and "Theory of Probability"
Should be read by every scientist. Two chapters of Jeffreys, an actual scientist, is worth the entire
oeuvre of professional statisticians or professional philosophers of science such as Popper or Kuhn,
who as HJ notes, seem to get their notions of physics from popular writings.
For example, HJ on the uncertainty principle in QM:
"The existence of errors of observation seems to have excaped the attention of many philosophers that
have discussed the uncertainty principle; this is perhaps because they tend to get their notions
of physics from popular writings"-Theory Of Probability.
HJ on statistical tests:
the use of P implies, therefore, is that a hypothesis that may be
true may be rejected because it has not predicted observable results
that have not occurred. This seems a remarkable procedure.”
(Jeffreys, 1961, p. 385)
After publication of translation of Boltzmann's paper, I came to
appreciate that his explanation of Entropy is much clearer than many
subsequent explanations, especially semi-popular or popular science
accounts. And of course it is absolutely accurate, as many accounts
aren't. In response I decided to write a short explication of entropy
that follows his approach, has only the simplest math in it, and is
hopefully both accurate and understandable by almost anyone with
minimal science background:
Entropy According to Boltzmann
Gregory Bateson's "Metalogue: Why things get into a Muddle" is
unsurpassed as completely non-technical, but surprisingly accurate and
witty explanation of Entropy and the Second Law
Bateson G (1972) Steps to an ecology of mind
(Ballantine Books, New York). p1 p2 p3 p4
Now teaching a tutorial Scientific Inference and Reasoning
as a trial
run for a Bayesian based course aimed to teach students how to really
analyze data and actually draw inferences and reason. Inspired by
Sivia's book "Data Analysis: A Bayesian Tutorial." Also to break from
the mindless application and misuse of 'classical statistics hypothesis
testing' that is still taught to students in spite of devastating
criticism in dozens of papers by prominant statisticians. And the NIH
wonder why there are rigor and reproducibility problems in biomedical
Case in point: 1994 Cohen in "The earth is round P<0.05"
explains exactly what is wrong, and has some recommendations
(personally I think he would have been on firmer ground if he gone
fully Bayesian). Twenty-two years later
Greenland S, et al. (Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations. European Journal of Epidemiology
(2016) 31:337.) "review 25 common misconceptions.... of significance
tests, confidence intervals, and statistical power" Twenty five of
them!! Surely this must give question to the traditional teaching
of statistical analysis of data, and suggest 'new' approaches such as
Scientific Inference and Reasoning now a course (BMB510) taught in
spring, complete with iPython notebooks and my text book.
Second Boltzmann paper translation published: https://arxiv.org/abs/1906.09221
Book contract with Springer for:
"Entropy and the Tao of Counting
A brief introduction to the second law of thermodynamics and statistical mechanics"