# Author: Briggs

October 28, 2008 | 23 Comments

## Probability of McCain win

This is a bit of a preview of a paper my friend Russ Zaretzki are working on.

Take a gander at his pic:

This is the probability that John McCain wins the election given only the historical evidence of Republican/Democrat elections, and the fact that there will be just 1, 2, …, up to 38 more Republican/Democrat elections. Let me explain.

Since Democrat James Buchanan ran against Republican John C. Fremont in 1857, United States presidential elections have been dominated by these two parties. From that first contest, Democrats have won 16 elections and Republicans 22. This year we have another election in which the two parties are again featured. Now, this means that the number of elections of this type has so far been finite, and history strongly suggests that this series of elections itself will be finite; that is, some day it will not be Democrats versus Republicans, or even might even be that there will be no elections1.

How many more elections there will be is, of course, an open question. But let us suppose that the one before us is the last election between the two parties. Then, conditional only on the past elections, the probability that the victor will be a Republican is 0.577. The standard Bayesian (continuous-value approximation) estimate gives 0.575. The classical guess is 0.579.

Our new method of guessing is based on knowing that the number of elections has been and will continue to be finite, that is, that it will not be without number, going on forever. It is important to recognize that traditional methods make this assumption. That is, that the number of “trials” (elections) will be infinite.

Ok, ok. These don’t seem like very big differences—and for this problem, they are not. But let’s suppose that instead of this being the final election, we’ll have two more. Then the probability McCain wins is just over 0.575. If we think there will be 9 more elections, then the probability McCain wins this one is only 0.570. Once the number of future elections becomes “large”, our guess matches the standard Bayesian one. That’s what the dashed, black horizontal line is. The red dot-dashed line is the classical estimate.

Eh, not a very big difference either, but it could be enough of one if you were, say, making a bet. And in some other problems, the differences are enormous; but this problem is a lot more fun.

The probability is over 50%. It obviously does not account for anything except previous elections. But it’s enough to raise a smile.

Incidentally, the math for all this is very heavily related to Laplace’s probability of succession. Google that. We introduce a twist that makes solving it sensible for certain problems. The surprise is that the probability depends on knowing the future number of trials (that’s the big difference).

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1 Ever notice that at the Democrat rallies you hear “Obama! Obama! Obama!”, while at the Republican ones you hear “USA! USA! USA!”?

## Stand by!

My book is coming!

It’s almost there, so let me tell you how modern math publishing works these days.

The author of course writes the work, and we all do it in a typesetting language called Latex (some just use Tex). Google it. It’s not different in spirit from web pages, which are content surrounded by “markup code” that tells the words where to go.

We can extend the analogy. Web pages are written in a markup code which is further subject to cascading style sheet rules. The style sheet rules say how big headlines are, what background images to use, and so on. In Latex, these are called class files (or “.cls” files).

Point of all this is that we write the words and math and the publisher provides us with a class file that does all the typesetting for them. Builds the Table of Contents, numbers all the pages and formulas, lays the footnotes properly, and so on, all automatically. Latex is sweet and orders of magnitude better than other word processing programs, such as MS Word.

But, unless you are a really famous author (not me), you are even given the privilege of writing your own Index! So, in math/physics/etc. books written with Latex, there is nothing for the publisher to do. They don’t even—again, unless you are famous—provide any direct copy editing. They let the authors do that, too.

Since I’m doing everything, I decided, a la Tufte1, to bring out the book myself. Most of the copies I sell will be to the students who are forced—er, elect—to take my class. This way I can keep the price way down.

When I was a visiting professor at CMU, the textbook cost, if you bought the “Solutions Pack” and “Calculator Guide” (or whatever it was called), was well north of \$100. 100 bucks! That’s nuts. Mine will be \$24.95.

The rest is done automatically, including uploading the text and sending it to printers, everything is actually pretty quick. The real time is in getting the book out to the distribution channels. So while my book will be available first from the publisher’s site, it will take from 1 to 2 months to show up on Amazon.com etc.

What do you do if you can’t wait? You can check out this book. My attempt at inserting skepticism into a strange field.

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1 Tufte does statistical graphics. If you haven’t seen his work, you should. His books, which are famous, are also non-traditional since there are, unfortunately, few statistical graphics courses at colleges. Still, he’s done OK with the books.

October 27, 2008 | 13 Comments

## An early start to the “holiday” season

From the Wall Street Journal comes the headline: “Retailers Expect Gloomy Holiday.”

Problem is, I have read the entire article—these kinds of stories seem to appear earlier and earlier every year—but I could find no mention of what “holiday” they meant.

There are some clues. The writer, Jennifer Saranow, more than twice mentioned “consumers” and wondered how much money these creatures will spend on “the holiday.” I am not sure what a “consumer” is, but it doesn’t sound good, in fact it sounds scary, which makes me think this “holiday” can’t be a joyful one.

I’d therefore guess the holiday was Halloween, an event filled with frightening creatures, but the article specifically mentioned “consumer” spending in the months of November and December, so that’s out.

Well, like I said, these articles appear with regularity once the weather turns cooler up here in the Northern Hemisphere, so I think we’ll see more of them, some of which might give us more hints about this mysterious “holiday.”

October 26, 2008 | 8 Comments

## Anybody see this one?

The book is The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice, and Lives by Deirdre Nansen McCloskey and Steve Ziliak.

From the description at Amazon:

The Cult of Statistical Significance shows, field by field, how “statistical significance,” a technique that dominates many sciences, has been a huge mistake. The authors find that researchers in a broad spectrum of fields, from agronomy to zoology, employ “testing” that doesnâ€™t test and “estimating” that doesn’t estimate. The facts will startle the outside reader: how could a group of brilliant scientists wander so far from scientific magnitudes? This study will encourage scientists who want to know how to get the statistical sciences back on track and fulfill their quantitative promise. The book shows for the first time how wide the disaster is, and how bad for science, and it traces the problem to its historical, sociological, and philosophical roots.

This is part of the theme I’ve long been pushing. McCloskey and Steve Ziliak are shocked, perplexed, and bewildered that classical statistics and p-values are still being used.

I’m not so shocked. They want people to abandon p-values and start using effect sizes. A fine first step, but one that doesn’t solve the whole problem.

I say we should drop p-values like Obama dropped Rev. Wright, eschew effect sizes like Joe Biden did reality, and return to observables. Let me, as they say, illustrate with a (condensed) example from by book.

Suppose there are two advertising campaigns A and B for widget sales. Since we don’t know how many sales will happen under A or B, we quantify our uncertainty in this number using a probability distribution. We’ll use a normal, since everybody else does, but the example works for any probability distribution.

Now, a normal distribution requires two unobservable numbers, called parameters, to be specified so that you can use it. The names of these two parameters are μ and σ. Both ad campaigns need their own, so we have μA and σA, and μB and σB. Current practice more or less ignore the σA and σB, so we will too.

Here is what “statistical significance” is all about.

Actual sales data under the two campaigns A and B is taken. A statistic is calculated: Call it T. It is a function of differences in the observed sales under both campaign. Never mind how it’s calculated. T is not unique, and for any problem dozens are available. With T in hand, the classical statistician makes this mathematical statement:

μAB

and then the infamous p-value is calculated, which is

Probability(Another T > Our T given that μAB)

where the “Another T” is the statistic we would get if we were to repeat the entire experiment again. Do we repeat it again? No, so we are already in deep waters. But never mind.

If the p-value is less than the magic number of 0.05, then the results are said to be statistically significant.

Quick readers will have spotted the major difficulty. What does equating two unobservable parameters in order to calculate some weird probability have to do with whether the campaigns are different than one another?

The words are not much, which is why McCloskey and Ziliak call the dependence on p-values a cult.

They recommend, in its place, estimating the effect size, which is this:

μA – μB.

Eh. It’s part way there, but it’s still a statement about unobservable parameters (and it still ignores the other unobservable parameters σA and σB).

What people really want to know is this:

Probability(Sales A > Sales B given old data).

Or they’d like to estimate the actual sales under A or B. There are new ways that can calculate these actual probabilities of interest. However, you won’t learn these methods in any but the most esoteric statistics class.

And that is what should change.

Because, I am here to tell you, you can have a p-value as small as you like, you can have an effect size as big as you like, but it can still be the case that

Probability(Sales A > Sales B given old data) ~ 50%!

which is the same as just guessing. Yes, the actual, observable numbers, the real-life stuff, the physical, measurable, tangible decisionable reality can be no different at all. At least, we might not be able to tell they are any different.

And that’s the point. The old ways of doing things were set up to make things too certain.

I wouldn’t go so far as to say reliance on the old ways was cultish. Most people just don’t know of the alternatives.