Day 8: A Stat(e) of Time

The hour is late. I have put this off until now. Time is of the essence. And upon time and the statistical analysis of time I will briefly turn.

Enough.

I am still on statistics in this post (as I was yesterday - UPDATE: John Shuster, skip of the US Curling Team, was benched).

I had a statistics professor (in psychology) who once used the Olympic sport of Luge to complicate the issue of statistics in research. In stats there is the concept of statistical significance. It is the way, for instance, that correlations are made. Is the number of times one thing correlates to another significant? Many things inevitably "correlate;" statistical significance is used to separate coincidences (things that happen by chance) from correlations (things are "actually" and consistently and predictably related).

Statistical significance, however, does not always manifest itself practically. In luge, for example, the differences in times between first and second and third and forth and fifth places rarely meet the conventions of statistical significance. Once you have to go out the thousandths of a second to determine the difference between first and second place, my professor would say, you are pushing statistical significance (of course there will be a difference in the times of each run, but are those differences significant?). That is, the differences between the times are not significant enough statistically to make any definite assertion about the relative excellence of individual lugers. To say it another way, a difference in race times that is one-one-thousandths of second doesn't "mean" anything statistically because it looks like simple chance. However, my professor continued half joking, it is the same damn German who wins every time. Statistically, the difference between first and second even third and forth and fifth shouldn't be significant - yet it invariably is, as we can point to dominant lugers who are always, on average, a few thousandths of a second faster than everyone else.

The point being, measurements are always choices made before the thing measured, and a good researcher, a good statistician, will be able to shift their measurement - to look around conventional statistical measures from time to time. I say this because too often, particularly in sports, stats are used as if they are argumentative trump cards. I do mean to say that stats are meaningless. Statistical significance is a positive boon in psychology, and it allows psychologists to accurately predict and describe (and thus positively impact) human behavior in many cases. Stats, however, are applied in order to certain achieve ends. Psychologists choose, based on convention, what level of significance is acceptable to reach a conclusion based on the results of a study. This does not make their results (or the choice) merely arbitrary, but it does mean that stats - far from settling arguments - are themselves arguments.