William M. Briggs, Statistician

It is finally done!

You may order directly from the publisher here¹. The book will also be available on Amazon, Barnes and Noble, etc. in about a month. I’ll update this post with the links when the book is in the distribution channels. Order as many copies as humanely possible.

Signed copies
I have had several requests for signed copies. I’ll be happy to do this for people. If you want a signed book, please email me at matt@wmbriggs.com. Please use “SIGNED COPY” as a subject line, and include your address in the body of the email. I’ll buy a few books from the publisher and then re-ship them out to people who want a copy. The charge will be the same as the publisher’s plus the same as they charge for Media Mail shipping and handling ($5.90), plus $1.15 cents (to cover tax). This makes the cost an even US$32.00. Payment will be arranged through PayPal (apparently, you don’t have to have an account to pay this way). I’ll send those who email me a PayPal “Request for Payment”; after that is received, I’ll ship the book (anywhere in the world).

Because I first have to order copies, sign them and then mail them out, it will of course take longer for you to get your book. I will wait a couple of days to see how many people email so I have a rough idea of how many books I should order.

I have two permanent places for news of the book:

1. My books tab (see upper right of screen): general news and information
2. Code page: free R code examples, erratum, links to papers, data, etc.

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Why is this book different?

Statistics has traditionally been taught decade after decade in a fashion that is long outdated. This book presents a brand new way of understanding probability and statistics at the introductory level. The approach taken does not require mindless memorization. There is very little math, and what there is requires nothing beyond multiplication and division. This book takes busy work out of standard statistics and puts insight back in.

Preface excerpt:

The regular readers of my blog, where parts of this book previewed piece by piece, provided razor sharp editing and keen questioning and kept me from making major blunders. So thanks to (screen names) Mike D, JH, Harvey, Joy, Noahpoah, Harry G, Bernie, Lucia, Luis Dias, Noblesse Oblige, Charlie (Colorado), Dan Hughes, Mr C Physics, Jinnah Mohammed, Ari, Steve Hempell, Wade Michaels, Raphael, TCO, Sylvain, Schnoerkelmanon, and many others (sorry if I left you out!). Any mistakes left are obviously their fault.

What’s next?
I use the book in my own classes, of course, and a few other professors have been either using a draft or have expressed interest in the book for their classes. If, by some miracle, the book becomes popular, I’ll start working on a “Answers to Selected Exercises” or, given that I get substantial comments from actual class use, a Second Edition. But that is all in the far, far future.

If you are a professor of a statistics class and want to chat about the book, send me an email at matt@wmbriggs.com and we can set up a time to talk. I have had great success with this approach for beginning students and can let you know how I run the class. Some guidelines are also given in the Preface.

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¹The cover art looks terrible on the publisher’s page. They have scaled it down from an enormous PDF to a small JPEG and it is pixelated. It looks great when printed, however.

I have a Samsung SGH-A707 cell phone. One of the free ones from AT&T/Cingular for signing a contract.

It is a piece of junk and I can’t recommend it. But that is neither here nor there. What is strange is the message I sometimes get on the phone.

It’s Halloween and the message is spooky. What happens is the phone gives one short vibration and then the words “Time To Die” flash on the screen.

Then the phone dies. It usually revivifies itself. It is dead yet not dead. It is an undead phone.

I have Googled this, but haven’t seen anybody else reporting it.

Could be one of the software designers was a Bladerunner fan.

Or it could be that my phone is possessed.

[Update: yes, there has been a title change. The old one was stupid.]

The other day, some weather geek friends of mine and I were exchanging emails about the early snow that was happening in Ithaca, NY.

It reminded Steve Colucci of the start of the Winter of ‘93. That one began with a snowstorm on Halloween and ended with the monstrous nor’easter in March. A particularly brutal year; a long, hard, cold winter.

It was the year Tom Hamill and I started as grad students at Cornell and took Colucci’s dynamics class. I recall trick-or-treating in graduate student housing with some families who had just arrived from Brazil. They and their kids had never seen snow before and were thrilled. They wanted it to go on forever. And it did. They weren’t so thrilled by January when, after yet another night of snow, they had to dig their cars out once more, only to come home and discover that the parking spot they had labored over so long was taken by somebody else. It was that year that I vowed to move to Texas.

This year has started like ‘93, but will it end like it?

Meteorologists often forecast by analogy. What’s that? Well, nothing more than looking at some pattern in the weather that happened sometime in the past, noticing that today’s pattern is similar, and then forecasting what will happen as what did happen. Weather weenies—the affectionate nickname given to those who memorize every storm since their birth—often use this technique to good success.

Chaos

Forecasting by analogue took a big hit once Ed Lorenz came out with his gorgeous paper “Deterministic non-periodic flow”, i.e. chaos. Lorenz was running a very simple weather model on a computer, storing its output, when that computer, as computers do, crapped out. Lorenz had to start over, and did, but he was surprised to discover that the results from the second run deviated strongly from the first run.

Lorenz started the second run with initial conditions that were, he thought, the same as in the first run. And they were, to several decimal places. Close enough! But those minute differences were enough to blow up to huge macroscopic differences in the output. This condition was eventually given the name sensitivity to initial conditions, and is why forecasting by analogy doesn’t always work. The small differences between the previous weather pattern and today’s could blow up so that tomorrow’s weather is nothing like what it was in the past.

So how much weight should we give the fact that this winter is starting out like the bad one in ‘93?

CPC

There is one group that asks these kinds of questions routinely. The Climate Prediction Center, a branch of NOAA. We are asking a question about climate here, and not weather, because we want to know what will happen over an entire season.

Here is the CPC temperature forecast for the three month period, December, January, February (DJF):

This format is a little screwy and takes some getting used to. Here is the idea behind it. Climate is actually a statistical phenomenon. It is something like an average of daily weather. To define climate requires picking bounds so we know when to stop and start averaging. The bounds are 30-year periods, starting and stopping on decades: thus 1971-2000 (or maybe it’s 1970-1999) is the period called the “climate normal”. Average weather/climate is defined with respect to this period.

This means that when you hear “Today’s temperature is above normal” it specifically is in reference to the climate normal period. Today’s temperature may not be considered “above normal” if you picked a different 30-year period. “Normal” doesn’t mean normal. There is nothing abnormal about any weather that eventually occurs. This is important to keep in mind when thinking about topics like global warming.

Now, since we have picked a reference set of data, we can use it to quantify our uncertainty in any outcome, such as the DJF average temperature. We can take the last 30 DJF temperatures and split them into three bins: a low, middle, and high. The splits are such that the 10 lowest temperatures are in the low bucket, the next 10 in the middle bucket, and the highest 10 in the high bucket. The CPC calls these three buckets, B for below “normal”, N for “normal”, and A for above “normal”; I used the scare quotes around “normal” to remind you that the word isn’t used in the same sense as its common English meaning.

With me so far? Historically, and by design, there is a 33 1/3% chance that any seasonal temperature will fall into one of the three buckets. Right? If you didn’t know anything about the future climate except what happened during the climate normal period, you would guess that there is a 33 1/3% chance that the seasonal temperature will be “below normal”, a 33 1/3% chance that it will be “near normal”, and a 33 1/3% chance that it will be “above normal.” Make sure you get that before reading more.

The CPC does know something about the future. It uses mathematical forecast models, analogy, expert opinion, chaos—yes, chaos—to predict what will happen. It can use chaos by running a forecast model based on certain initial conditions. They then “perturb” those initial conditions slightly such that the perturbations are in line with the uncertainty in the measurement of those conditions, and then run the models again. They do this many times, each model run beginning with different initial conditions. At the end, you can take something like an average of all the model runs. This process—which I have barely sketched—is called ensemble forecasting, and is an area Tom Hamill has devoted his career to, producing a lot of significant results.¹

Anyway, the CPC then takes everything it knows about the future climate and then uses it to adjust the probabilities the temperature will fall into one of the buckets. They do this for many different points over the United States. If there is an area in which they believe they can say nothing useful, they do not change the bucket probabilities. For example, look at West. That area is all white, indicating that there is no useful information in the forecast models that change the probabilities. Thus, for this coming DJF, there is a 33 1/3% chance the temperature will fall in the B bucket, a 33 1/3% chance it will fall in the N bucket, and a 33 1/3% chance it will fall in the A bucket. Just the same as you would have guessed knowing nothing but the climate normal period.

Now focus on Wisconsin. There is an “A” inside a “50″ contour line. This means, for that area, the CPC says there is a 50% chance the DJF temperature will fall in the A bucket. It still means a 33 1/3% chance that it will fall in the N bucket, but it must mean that there is only a 100 - 50 - 33 1/3 = 16 2/3% chance it will fall in the B bucket. The N bucket is almost always left along, and only the A and B buckets are adjusted.

What about Texas? It has an “A” and inside a 33 1/3% - 40% contour, meaning what? Right. They haven’t adjusted the probabilities much at all, say + 3%. We can notice that there are no “B” areas on the map, which means they do not think any area has an increased chance that the temperature will be below “normal.”

All this means is that the CPC thinks the DJF period, if anything, has a higher chance to be warmer than normal in some Great Lakes areas.

There are also precipitation amount forecasts. Click here to see the pcp (shorthand for precipitation—if you want to sound like you know what you’re talking about, never say “precipitation”, say “precip” with a long “e”; you’ll be taken for a real meteorologist).

Does it work?

This is the question. If you are using any prediction/forecast/statistical model you must ask whether using it adds any value. This is true for weather and climate forecasts and for any other quantity you care about: stocks, your health, test scores, and on and on.

The true mark of usefulness is skill. Skill represents improvement over “just guessing.” You should calculate skill of any statistical model that you use, whether or not it built for forecasting (all statistical models are forecasting models, but that’s a subject for another day).

For the CPC forecasts, skill means beating the “climate normal” guess; that is, the guess of 33 1/3% for each bucket. If the CPC cannot beat saying, essentially, “I don’t know”, then the forecast should not be used. If the CPC forecast does not have skill, it means you will do better by ignoring it.

Now, skill is a score of some kind, and there are many skill scores. Many are ad hoc, created because their users thought they sounded good. Some skill scores can give a false impression of the true value of a forecast/model. The probabilistic behavior of skill scores is a tricky business and quickly leads to surprisingly deep math. (I know, because this is my area, and I often find myself swimming in uncharted waters.)

Dan Wilks, of Cornell, has spent some time investigating the skill of CPC forecasts. He has found that the one-month ahead forecast has modest skill. Forecasts for longer lead times have some skill, but not much, and it quickly dies out. He found that there is no skill after about 12 months.

Here is the CPC’s assessment of their own skill:

They use something called the “Heidke skill score” (search for the term on that page). It is not what I would have chosen since it is, I think, suboptimal in this case: it will exaggerate performance. Nevertheless, let’s go with it.

The score must be above 0; scores below 0 mean the “I don’t know” forecast did better. Look only at the blue line: this is the skill you’d get it you relied on the CPC forecast routinely. The red line only calculates skill for those areas in which they adjusted the bucket probabilities: this has some use, but it is not the true skill that a forecast user would see.

The blue line is mostly above 0 (the dashed blue line is the average score over this time period). There is some semi-periodicity in the skill lines. Some of this is due to know causes like the El Nino and La Nina phenomena. Other causes aren’t known (if they were, then they could be forecasted!).

Overall, not a terrible performance, but not stellar either (recalling the Heidke score exaggerates a bit). It’s very very hard to predict climate. But at least the CPC is open and up front about their performance. They show their skill right next to the forecast and so earn a lot of respect because of this. Also, contrary to what you might have heard, meteorologists are pretty good about guessing the future. As long as that future is not too far off.

Store the nuts or not?

The CPC says, for most areas, “I don’t know.” The analogy says, “Look out!” The—very badly behaved and misleading—gambler’s instinct says, “Well, we haven’t had a bad winter for a long time, so we’re due for one.” The Farmer’s Almanac, a periodical written by trolls in some sub-basement completely disconnected from reality, says “Could be a bad one.”

I won’t tell you my forecast. I will tell you I bought a brand new, thick overcoat.

See you in the Spring!

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¹The advances have been mainly in weather and not climate models. The models that you hear about predicting global warming have not reached state of the art with respect to ensemble forecasting.

A friend sent this:

Say, if the Big O does win, will MoveOn.com change its name? To, maybe, StayWhereYouAre.com?

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!”?

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.

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.”

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 doesn’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 μ_A and σ_A, and μ_B and σ_B. Current practice more or less ignore the σ_A and σ_B, 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:

   μ_A=μ_B

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

   Probability(Another T > Our T given that μ_A=μ_B)

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:

   μ_A - μ_B.

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

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.

Take a look at his picture:

This is from an article by “The Numbers Guy” Carl Bialik at the Wall Street Journal. The story is about how life expectancy calculators are not terribly accurate. This really isn’t much of a surprise, but the picture should be.

This is because both presidential candidates, and of course many other people, nervously claim that there is a “Health Care Crisis! We have to do something!”

Yes, it’s so bad that the people are living longer and longer and longer… This picture says that whatever the crisis is, it clearly doesn’t have to do with that part of health that keeps people alive. I would argue that that part is the most important; apparently, others disagree.

This is another example of the phenomenon that the better things get, the more people complain. Or maybe people don’t complain more, they complain at the same rate, but because things are better, the complaints are about matters and points that are increasingly trivial.

Hasn’t somebody given a name for this dynamic?

Read Ted, White, & Blue: The Nugent Manifesto. You will not agree with everything, some or lot of it might even infuriate you, but you not regret the time spent.

If you don’t know much about Nugent other than he’s a rock musician, let me tell you something you might not believe. Uncle Ted can write, and write well. His prose crackles with the same energy as his music. The words on the page flow; they practically jump off and get in your face. They force you to think, especially if you don’t agree with him. You can’t just say (to yourself), “He’s wrong.” He makes you say why. Doesn’t sound like much, but it’s no small trick.

Unlike just about every other celebrity over the last forty years, Uncle Ted lives clean and sober and always has.

Punks used to laugh at me, said how can you rock and not get high? Well I just stood my ground, and I watched those assholes fall and die. Cuz I just wanna go huntin, it makes me feel so good. I just gotta go huntin, try to find me in the woods.

Nugent is the ultimate environmentalist, a staunch steward of wildlife, an ardent advocate of hunter’s rights, and of the rights of the rest of us to own guns. This book isn’t about these things, but his previous book, God, Guns, & Rock and Roll, is. So if you’re looking for tips about the best kind of arrow head to bring down a buck, or you want to know why Fred Bear was a hero to so many, buy that book and not this one.

Ted, White & Blue really is a manifesto. Here are some of his non-standard proposals:

• Refuse to fund healthcare for people who don’t care about their health…
• Eliminate all welfare except temporary benefits for military personal and their families. Able-bodied Americans who refuse to work will be sent to Cuba, Mexico, England, and France.
• Create a $100,000 reward for any U.S. citizen who shoots and kills a paroled felon during an assault or home invasion.
• Eliminate the IRS, institute a national sales tax, and force the U.S. government to live within a budget tied to actual revenues.
• Pass a constitutional amendment limiting citizen employment in the federal, state, or local government jobs to 5 percent of the U.S. workforce.
• Remove and open all levees and dams in New Orleans and make people live on high ground. Give no more handouts to cover stupid mistakes of any kind.
• Make it illegal to sue any business simply for the criminal misuse of their legal product.
• Encourage all states to expeditiously execute all convicted child molesters.

I love the constitutional amendment idea.

God gave man a soul; a powerful, instinctual moral and intellectual True North compass that completely differentiates us from all other living creatures….It is soulless to forbid a good citizen the right to carry a gun for self-protection while you dare to actually charge that citizen (subject) to pay for your armed security detail, Ted Kennedy.

Nugent is one of the few—left or right—willing to say things like this: “If you are pissed off about where you see the country going, remember this: you are to blame.” And this “Caring about a problem doesn’t solve a damn thing.” Raising awareness anyone? “[S]ome goofy Americans somehow they are entitled to these material creature comforts even though their wages cannot support this non-essential junk.” “Nobody owes you a thing. Nothing. Zilch. Zero. Nada. Everything you will get out of life will be based solely on what you put into it…You will get no more than what you are willing to bust your ass to earn.”

He is not a Republican nor a Democrat. His definition of televangelist is religious pimp. He isn’t anti-religion: “Let us all pray for good bombing weather.”

Uncle Ted made an huge, enormous mistake when he was a young man. He purposely got himself out of the Vietnam draft. He knows he must answer for this moral crime. I am tempted to say, a la the media, this “youthful indiscretion”, but I won’t, because what he did was wrong. The only question now is: can we forgive him?

Regrettably, I did not serve in the military upon graduation from high school. For that I am truly sorry…I admit to self-imposed, near-total insulation from worldly truth and the reality of Vietnam…This is no excuse for my woeful and deep disconnect from the critical events of the world–and I don’t offer it as one—but it is the truth…In order to provide some sort of restitution for my youthful disconnect, I have done what I can over the years for members of the armed forces…My enlightenment, though slow in coming, eventually arrived.

Four of my uncles served during the Vietnam war; my dad and another uncle were out of the Navy and Army before the war got going. I naturally served long after, but all my senior NCOs and officers were veterans¹. I know how much it means to have people remember those who are serving. When I was in Okinawa, word had it the the folks at ESPN would mention our base on Sports Center one night. They did. A trivial thing, really. But everybody was very proud. Nugent’s concerts for the military in Iraq are deeply appreciated.

Some more Nugentisms. He does not want a fence built along our southern border, but would deport anybody who comes here illegally. “You don’t have a right to heath care. Got that? What you have is a personal responsibility for it.” “When was the last time you heard, read, or saw a story in the media that reported a citizen using a gun to stop a crime?” “Never forget you have a duty, not a right, to defend yourself and your family.”

Counting on ethanol to replace gasoline is akin to believing that rap is a legitimate musical genre, violent thugs deserve to be let out of their cages, or that animals have rights. Dogs chasing their tails make more sense than this bureaucratic KLSTRPHK. Ethanol is a joke.

Nugent has said that he is considering a return to Michigan to run for governor. Would you vote for him?

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¹None ever flipped out, went insane, became crazed, nor in other ways emulated Oliver Stone.