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Category Archives: Statistics

Amidst a poker game last night, I posited the following:

Every poker player has worse than average luck.

Call it the anti-Lake Wobegon Effect (aLWE).

This claim, at first glance, is entirely absurd.  Treating poker as a zero-sum game, one player’s good luck must be offset by another player’s bad luck, such that not all players can possibly have below average luck.  We learn this in kindergarten, and then again in advanced college mathematics.  So why am I trying to argue something that is patently absurd?  Below I will argue both why it is wise to believe aLWE, and also reasons why it may be true.

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  • Tea Party voters’ embrace of Rick Santorum largely perplexes me.  I feel completely baffled as to how these individuals think, and don’t know how to learn.  Will Charles Murray’s book help on this front?
  • This post from the Cato Institute and this and this from Ezra Klein have me thinking that the difference between right and left on fiscal policy is much smaller than I’d thought.  Is it just me, or are these gaps remarkably small?

 

I’m puzzled by this chart from the Brookings Institution‘s Hamilton Project, which attempts to predict how long it will take the United States to return to pre-recession level employment.  The chart plots three scenarios: a pessimistic option, in which employment grows at 208,000 jobs per month, as it did in 2005; an optimistic option, in which employment grows at 472,000 jobs per month, as it did in the best month in the 200s; and a middle option, in which employment grows at 321,000 jobs, as it did in 1994.  The takeaway from the graph, presumably, is that it will take a very long time to return to full employment.  The problem with the graph, is that its assumptions are entirely arbitrary, to the point that its predictions are largely meaningless.

The function of science, or social science is to use observed data to create theories that make predictions.  In this case, Hamilton is observing the period 1990-2008, a period of time that neither included nor followed a large recession, then theorizing that 2012-2025, a period of time that does follow a large recession, will behave like 1990-2008.  Hamilton is essentially saying that because job growth never exceeded 472,000 jobs per month when unemployment was low, it cannot possibly exceed 472,000 jobs per month when unemployment is high.  It’s just bad science, and it’s exactly the same bad science that failed to predict the recession in the first place.  Any scenario planning based on historical data leading up to 2008 would have deemed it impossible that employment would fall by 12 million from 2008 to 2010.  Why then, does Hamilton continue to use a forecasting method when that method’s limitations have been so clearly exposed?

I think that this story is much more important than this story.

I say these statistics are much more important than these statistics.

The more I think about this Andrew Gelman post, the more ridiculous it seems.  Gelman argues that economists, especially popular economists, use a pair of contradictory arguments to explain phenomena:

1. People are rational and respond to incentives. Behavior that looks irrational is actually completely rational once you think like an economist.

2. People are irrational and they need economists, with their open minds, to show them how to be rational and efficient.

In the comments, he clarifies his position:

My problem with some pop-economics is not with the use of arguments 1 or 2 but rather with what seems to me as the arbitrariness of the choice, accompanied by blithe certainty in its correctness.  This looks more to me like ideology than science.

I have no problem criticizing economists for their blithe certainty, a criticism I’d also to apply to just about everyone, myself included.  But I don’t follow Gelman’s criticism of the fact that economists apply different models to different situations.  This happens in all disciplines, including Gelman’s field of statistics.  For instance, statisticians often apply one of the following arguments:

  1. Phenomenon X follows a normal distribution.
  2. Phenomenon X follows a log-normal distribution.

1 and 2 are entirely contradictory, and to a non-statistician, it would appear entirely arbitrary whether to apply 1 or 2.  But to a statistician, there is a logic (part science, part art) as to whether to apply 1, 2, both, or neither.  Similarly, it may appear arbitrary to Gelman whether to assume rationality or non-rationality in a particular situation, when there is a consistent logic apparent to economists.  Ultimately, models should be judged based on the reliability of their predictions, not perceived arbitrariness by outsiders.

One purpose of this blog has been to question the way to think about problems.  I’m specifically interested in how philosophy of science, statistics, and rhetoric shape the way we think.  Another purpose has been to identify which world problems are most worthy of discussion, having received insufficient attention.

Today I followed Tyler Cowen’s link to Mike McGovern’s essay about development economics.  Development economics is a topic I don’t understand well but consider highly important and under-discussed.  It’s also a meta-analysis, exploring different ways to think about problems.  For instance:

The difference between poets and economists…there is an acceptance that there are many ways to write a great poem, just as there are many enlightening ways to read any great poem. Bound as it is to the model of the natural sciences, economics cannot accept that there might be two incommensurable but equally valuable ways of explaining a given group of data points…Paul Collier, William Easterly, and Jeffrey Sachs can all be tenured professors and heads of research institutes, despite the fact that on many points, if one of them were definitively right, one or both of their colleagues would have to be wrong. If economics really were like a natural science, this would not be the case.

I wasn’t expecting to find philosophy of science (or philosophy of poetry) in an essay about third world development, but I think this type of thinking is necessary to address the particulars of third world development.  It’s a slightly morbid point of view; most people who want to solve problems want to do something; instead I want to think about thinking.  But actions are driven by views, and views and driven by the way we think about the world; when we don’t analyze the ways we think, we’re more likely to hold misguided views and take misguided actions.

McGovern’s assessment of development economics is shaped by his philosophy of science; in the above paragraph, he first criticizes economists for trying to be scientists, and then criticizes economists for being bad scientists.  The two criticisms contradict, and don’t account for the fact that throughout history, hard science regularly maintains contradictory points of view, whether in cosmology or mathematics.

My concerns about McGovern’s philosophy of science should dismiss what’s he written; his concerns about development economists may have more to do with their rhetoric than their scientific thinking.  On the whole, his essay is a really interesting read, and I’ll continue to think about it throughout the day.

Happy July 4th.

I find this research from Pew really interesting, as it tries to segment voters beyond a one-dimensional political spectrum.  Segmentation can produce meaningful results, but it can also be misinterpreted.  For instance, consider this chart:

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