'Statistics
is the 'Science of Uncertainty",'

Noel Cressie and Christopher K. Wikle, Statistics for Spatio-Temporal Data, Wiley, 2011, p. 4

Noel Cressie and Christopher K. Wikle, Statistics for Spatio-Temporal Data, Wiley, 2011, p. 4

Uncertainty is all around us; we can't expect certainty. But uncertainty can often be "quantified" -- that is, we can talk about degrees of certainty or uncertainty. This is the idea of probability: a higher probability expresses a higher degree of certainty that something will happen.

Statistical techniques are designed to help us understand areas where uncertainty is present and can be quantified. Most statistical techniques are based on probability. W. Edwards Deming, a pioneer in the use if statistics in industry, said, "It is his knowledge and use of the theory of probability that distinguishes the statistician from the expert in chemistry, agriculture, bacteriology, medicine, production, consumer research, engineering, or anything else."

Contemporary statistician Xiao-Li Meng reiterates and expands on this idea, using the words "randomness" and "variation" instead of uncertainty:

Statistics,
in a nutshell, is a
discipline that studies the best ways of dealing with randomness, or
more precisely and broadly, variation. As human beings, we tend to love
information, but we hate uncertainty -- especially when we need to make
decisions. Information and uncertainty, however, are actually two sides
of the same coin. If I ask you to go to the airport to pick up a
student you have never met, my description of her is information only
because there are variations; if everyone at the airport looks
identical, my description has no value. On the other hand, the same
variation causes uncertainty. If all I tell you is to pick up a Chinese
female student ..., then my description is not informative enough
because it still allows too many variations. There may be a substantial
number of individuals at the airport who look like a Chinese female
student. ^{2}

Consider
the theory of evolution,
for example. Biologists are in general agreement with regard to its
essential correctness, but the evidence marshalled in favor of
evolution
is quite unlike the kind of evidence used in mathematics or physics.
There is no way to prove that evolution is correct in a mathematical
sense; the arguments that support it consist of (to borrow the title of
one of Pólya’s books) “patterns of
plausible reasoning”, along with the careful consideration of
alternative hypotheses. In effect, biologists have said the following:
“We have mountains of evidence that are consistent with the
theory, broadly construed; there is no clear evidence that falsifies
the proposed theory, and no rival hypotheses meet the same
criteria.” ^{3}

In other words, in many areas, we can't expect certainty, or even anything approaching it, from a single study. But an accumulated body of evidence based on high quality research can give us a high degree of certainty. Working well in a field with high degrees of uncertainty requires patience and often humility while the mountains of evidence accumulate -- and might not turn out to support our pet theories. Statistician Howard Wainer said it well on the last page of one of his fascinating books on visual representations of data:

…
to deal with uncertainty successfully we must have a kind of tentative
humility. We
need a lack of hubris to allow us to see data and let them generate, in
combination with what we already know, multiple alternative working
hypotheses. These hypotheses are then modified as new data arrive. The
sort of humility required was well described by the
famous Princeton chemist Hubert N. Alyea, who once told his class,
“I
say not that it is, but that it seems to be; as it now seems to me to
seem to be.”^{4}

In case you need more support to convince yourself or someone else to give uncertainty the respect it is due, here are some more quotes about uncertainty. And/or try the Radiolab episode on Stochasticity, or David Spiegelhalter's Times Online article and video on uncertainty in science, or David Aldous's Annotated list of contexts where we perceive chance, or Charles Seife's

For speculations by a neurologist (and engaging writer) on why we have so much difficulty accepting uncertainty, see Robert Burton's On Being Certain.

- One consequence of not taking uncertainty seriously enough is that authors often write results in terms that misleadingly suggest certainty. For example, some authors might conclude from a study that a hypothesis is true or has been proved, when it would be more correct to say that the evidence supports the hypothesis or is consistent with the hypothesis.
- Another consequence is (mis)interpreting results of statistical analyses in a deterministic rather than probabilistic (also called stochastic) manner.

1. Deming, W. Edwards, Walter A. Shewhart, 1891 - 1976, Amstat News, September, 2009, p. 19

2. Meng, Xiao-Li, Statistics: Your Chance for Happiness (or Misery), Amstat News, September, 2009, p. 43

3. Schoenfeld, Alan, Purposes and Methods of Research in Mathematics Education, Notices of the American Mathematical Society, v. 47, 2000, pp. 641 - 649. Available at http://www.ams.org/notices/200006/fea-schoenfeld.pdf

4. Wainer, Howard, Picturing the Uncertain World, Princeton University Press, 2009, p. 210.

5. Seife's book Proofiness: The Dark Arts of Mathematical Deception (Penguin, 2010) is also a good read.

6. Burton, Robert (2008). On Being Certain: Believing You Are Right Even When You're Not, St. Martin's Press.

Last updated Sept 25, 2011