William M. Briggs

Statistician to the Stars!

Page 395 of 540

Amish Romance Meets Teen Vampire Fiction

In a Books-A-Million in Lakeland, Florida I happened down an entire row, yards and yards long, of Amish Romances, a genre I had heard of but scarcely believed existed. Each book featured a bonneted woman staring pensively into the middle distance, her face hovering over a farm on which a horse and buggy are seen driving away.

But to get to that row, I had to pass through an aisle of Teen Fiction—hundreds more titles, each evidently staring a vampire-as-superhero-slash-seducer, Twilight style.

As I thought about the popularity of all these books, it hit me: a new genre, which must be a publishing sensation.

Vampire Amish!

And so I preview for you today, the opening scene of my forthcoming novel: Lancaster County’s Dark Secret.

———————————–

Jacob Burkholder glanced at his schedule and double-checked the room number for his first period Chemistry class. The teacher was Mister Harding, who, rumor had it, was Harrisburg High’s easiest ‘A’.

Jacob needed a soft class because this was his first semester in an actual, public school. One that was more than just a single room and shared by children of all ages, that is.

This was because young Jacob was Amish. He was sixteen, out living among the English on his rumspringa, the time of youthful rebellion where Amish young men and women decide if they want to remain part of the fold or forever leave it behind. Whether they would accept the Baptism or renounce the Order and be forever sworn to secrecy.

Jacob knew he would soon have to make the choice. He was thinking about this intently, so distracted that he walked right past his room and into an open locker door.

“Hey, look out, freak” said a burly jock named John Prangle, pushing Jacob against the wall. Prangle’s three friends laughed. “Learn to watch where you’re going. You trip on these clothes of yours?” He grabbed Jacob’s shirt. “What’d you do, make this yourself? Stylish.” He laughed and the four went into the room.

Steady, Jacob thought to himself. Let it go. He knew even though he had not yet had the Baptism that a flick of his wrist would have sent Prangle sprawling to the floor. But then they would know. The Order would know. They would somehow find out. They always did.

He let the moment pass, but in his anger he had unknowingly ground the pencil he was holding to dust. He held up his hand and let the yellow sand slowly pour from his fist. He allowed himself a slight smile.

It was then he heard what no other ear could have. A small gasp, filled with longing.

It came from Isabella Springer, who stood in the doorway of the biology class. She had watched the entire incident without fully comprehending what had happened. She saw Jacob sprinkle the pencil dust, but that was not, as Jacob mistakenly thought, what caused her strangled cry.

It was seeing Jacob for the first time that forced her sharp breath. She had never seen anybody who had looked like him before. He is beautiful, were her first thoughts. It was true she had known other boys who were handsome, but never before had she beheld such raw, unadorned physical perfection. His features glowed. It caused her physical pain to look at him; yet she couldn’t remove her eyes. He is too beautiful.

Jacob returned her stare when the attendance bell rang. Several late-coming students pushed past them into the room. Jacob followed, avoiding Isabella’s gaze as he did, not trusting himself to talk. Isabella’s throaty, soft moan was still echoing in his head.

What Jacob didn’t know was that Isabella was also new to Harrisburg High. This was her first semester since she had moved from a suburb of Philadelphia. It was just six months ago that her parents died in a car accident in upstate Pennsylvania. The police said her dad had been drinking, but she didn’t believe it. She never saw her father have more than one beer a night. Isabella lived with her maternal grandmother now.

In the classroom, while sneaking a look at Jacob, Isabella accidentally pushed her folder into a glass beaker, which crashed to the floor and shattered. Mr Harding hushed some boys who started laughing, and then went to the closet to fetch a broom.

Isabella leaned down and began pushing the broken glass into a pile with the edge of her folder. As she did so, a shard caught her on the finger, cutting it. “Oh!” she said.

Jacob’s attention immediately riveted on Isabella. He closed his eyes and breathed deeply, barely suppressing a moan. He was starting to feel his face turn hot and red. He couldn’t take much more before…

He stood up and bolted from the room, crashing into Mr Harding who was returning with the broom.

“Ha! Look at the pansy run,” said John, “Scared of the sight of blood!” He and his crew laughed.

Isabella’s eyes followed Jacob out. After he was gone, she held up her finger, which was still bleeding slightly. “He’s not frightened.”

Somehow she knew.

———————————–

If you want more, have a publisher contact me. This story simple cannot lose.

It’s national Pass On The Briggs month here at wmbriggs.com. If your interpretation of this phrase is on the generous side, email a link of this page to a friend who hasn’t been here before. The best kind of friend is one who has need of a statistician and who has a lot of money.

Warnings by Mike Smith

Mike Smith :: Warnings

Warnings: The True Story of How Science Tamed the Weather

by Mike Smith

Buy here.

I met Mike Smith in 1994 or 1995 at an American Meteorological Society annual meeting. I tried to convince him of some crazy idea I had about making money off of short-term climate forecasts, made available monthly from the then Climate Prediction Center.

He showed the good business sense he was known for by saying, in the politest way imaginable, that I was nuts. He paid for the beer, though, which shows his generosity. As does his sending me a copy of his new book, Warnings. You might have also seen Mike as a commenter to the blog.

Like a lot of weather geeks, Smith became interested in the weather by experiencing it. In his case, the hard way: a tornado. One, unannounced, whipped through his hometown when he was a boy in the 1957. His family made it, but a lot of others didn’t.

The reason many were wiped out was that the Weather Bureau, as the National Weather Service was then known, was forbidden to issue tornado warnings. Or even to use the word “tornado.” Reason? Fear of false alarms and panics. The bureaucracy at the time thought a few dead bodies here and there was better than the bad publicity of a possibly blown forecast.

It was a combination of the efforts of private media and a long string of bad luck in the form of deadly tornado outbreaks that finally knocked some sense into the bureaucracy. Nowadays, of course, tornado warnings are considered reliable and lifesaving.

Smith started off as a television weatherman and was there at the beginning of broadcasting radar images to the public. In color! He turned his forecasting skills into profit when he started WeatherData, a company that provided custom predictions for corporate clients like train transport firms. Knowing where to avoid high winds and floods saves them a lot of money. Smith recently sold WeatherData to Pennsylvania-based Accuweather.

The story is Ted Fujita’s as much as Smith’s. Fujita’s passion was storms and storm damage. It was he that devised the well known rating scale, F0 to F5, used to classify the strength of a tornado coupled with the amount of destruction it wreaks.

In his work, Fujita pored over an immense number of photographs, compiled eyewitness reports, and assimilated massive amounts of weather data. Not just to classify tornadoes, but to show where hot spots hurricanes are. But his ideas were always regarded with initial suspicion; he always had to swim upstream.

His greatest success come from persevering through the stiff criticism of consensus, which assured the world that microbursts were fictional. These are rapid, powerful downdrafts of winds accompanying some storms. Through intense skepticism, Fujita proved that microbursts were responsible for many crashes of jets, usually on takeoffs and landings.

Meteorologists and the FAA eventually succumbed to weight of evidence and installed specialty radars at airports that could “see” wind; since then, no more crashes due to microbursts. Not that bureaucracy was defeated. The FAA still does not directly share its weather data with the public or with the NWS.

Smith includes some technical details, but very few; not enough to turn off non-mathematical readers. For example, he points out that when a gram of water vapor condenses it released 540 calories of energy, which is, of course, true. Think of it as returning the energy used to turn the liquid water into gas.

Most people, I think, don’t have a good feel for how much energy a calorie is. Smith gives the hint that supercell thunderstorms release megatons on sheer power, so those grams of condensing water really add up!

And we never learn why locating radars higher off the ground results in more ground clutter return and not less (there is more backscattering from the radar beam sidelobes). But these are trivial criticisms.

When I was starting out in meteorology, I recall talking with other weather-types about what would happen if New Orleans ever got whacked by a hurricane: they’d be dead meat. And everybody knew it. Except New Orleans and Louisiana politicians, who chose not to know it, or to forget about it.

Yet Hurricane Katrina—which was as well forecasted as a storm can possibly be—still took the appalling mayor Ray Nagin and other officials by surprise. Smith leads us through the tragedy of ineptitude step by step. One example: why were firefighters and other rescue workers a day late? They first had to stop in Atlanta to take a mandatory course on sexual harassment before they were allowed in the field. Ah, the wisdom of government and delights of political correctness.

Still, the overall story is one of success. Improvements in the science of meteorology and in the technological tools have certainly saved a lot of lives. And the future appears bright.

2000 Scientists Demand Climate Action! Part II

Read Part I

Cap & Trade & Tax & Spend is not dead. It was never even ailing. It’s healthy and alert and standing in the corner, unnoticed. It has on its face a grin of the type we can’t mention on a family blog.

Senate Democrats worked with Al Gore, and he cobbled together a list of 2000 people (some scientists, some not) to sign a letter demanding action on “climate change”, a.k.a. global warming. In Part I, we saw that many of the letter’s signers were economists.

These economists are being shifted to the front line to mask the climatologists behind them. This is because climatologists have, over the past four months, received a series of black eyes; right now, they’re not too pretty to see.

Make no mistake: the main arguments you will hear shortly in support of Cap & Trade & Tax & Spend will be economic and not scientific ones. We therefore must understand the nature of these arguments.

Yesterday, we examined briefly how conditional probability should be used in unwinding economic predictions so that we can estimate their true likelihood. That is, usually economic statements are said to be true with a certainty far greater than warranted.

An economist might say, “If we don’t do something about global warming, then we’ll lose over three million jobs a year starting in 2020.” This sounds like certainty: we will lose at least that many jobs etc. It is not certain.

At the least, this prediction is conditional on the belief that harmful man-made global warming (HGW) is true. It is not. Even the IPCC says that HGW is only 90% certain. That simple fact means that our economist’s prediction can be no more than 90% certain, and is probably far less certain than that.

That prediction also relied on a string of assumptions of how job loss is related to temperature (and perhaps other climatological variables). None of these assumptions is certain. In other words, each assumption is only probable.

If each assumption is logically independent from each other assumption, then the probability that each assumption is true can be multiplied together. The resulting product will say how likely the entire prediction is.

For example, a typical economic model might have a dozen assumptions, plus the assumption that HGW is true. If we generously suppose each assumption is 50% likely, and since we’re nice people, we’ll only suppose there are four assumptions, then there is only a 5% chance that the prediction is true. This comes from 0.5 multiplied by itself four times and then multiplying that by 0.9.

The prediction, therefore, has gone from scarily true to probably wrong.

The situation is more complicated when each model assumption is not logically independent; which they usually are not. When they are dependent, the string of them is more likely than when they are independent. How much more so depends on the assumptions themselves. There is no general formula, of course, so each prediction must be handled individually.

However, since any economic prediction conditional on HGW will contain a large number of assumptions, the chance that any prediction is true is probably still small.

But what if there are dozens of predictions, each equally dire? Shouldn’t the sheer quantity of doomsday scenarios cause us to worry?

No. Uncle Mike in a comment to Part I presented an example, which I’ll modify here. We have a die which we’ll toss once. Given the usual premises, the chance that it shows a six is 1/6. Thus, if I predict a six will show, there is a 17% chance I’ll be right.

Now suppose that you and I both predict a six will show. What is the chance that at least one of us is right? It is still 17%. We are predicting the same thing and we are both using the same theory, by which I mean the same premises. You could equivalently say that we are using the same model. Or—yet one more way to say it, and one that uses the language of both parts of this article—we are both conditioning on identical information.

Our predictions are not logically independent: they are the same thing. The chance that at least one of us guesses correctly is still 17% if you and I and your mom also predicts a six. Or if a million people predict a six, or if every person on the planet does. The chance that at least one of us is right is never better than 17%.

So when we hear hair-raising predictions coming from all quarters, we can’t assume that the probability that at least one or more of them is true is high just because there are so many of them. While each horror has slightly different information than its brother, at base they are all conditioning on much the same information.

The voices in the choir are all singing the same song.

Read Part I

2000 Scientists Demand Climate Action! Part I

Let’s suppose everything you have heard about global warming is true.

It really will warm by a half-degree Centigrade by 2050, or even by three-quarters of a degree, or whatever. Some areas will have slightly warmer nights, just like you’ve heard. And some places will find themselves just a bit wetter, others a bit drier.

Very well, global warming is true. Now what?

Well, not much. To understand why, we’ll have to understand conditional probability. But hold off on that for a moment.

Because first we have to comprehend the 2000 scientists who signed a letter demanding the Senate “take action” on climate change. Presumably, by “climate change” they meant global warming. But never mind.

Eight of the signers were “Nobel laureates.” Not Nobel winners in meteorology, climatology, or oceanography, of course, because old Alfred was unaware of those subjects. One Nobeler was Leon Lederman, 88, a retired neutrino physicist, who said, “if anything, the climate problem is actually worse than reported earlier.” Scary stuff.

Of most interest is that some of these “scientists” were not scientists. Many—I don’t know how many—were economists. And that is why we must understand conditional probability.

What do economists know about long-range climate forecasts? Nothing. Or, rather, nothing that the average citizen doesn’t know. In their training, economists learn the best way to disparage the Laffer Curve, but the equations of motion and radiative transfer never appear on their syllabi. So why did they sign?

Economists are like the Congressional Budget Office: they have to believe what they are told. Their models are conditional on assumptions given them from on high. So, if a climatologist says, “It will be a half-degree Centigrade warming by 2050″ the economist has no choice but to believe him.

The economist says, “OK, it’s going to be slightly warmer. How does that affect my bottom line?” And off he goes, builds his models, and concludes “More study is needed. I’ll need more research funding.”

No, only kidding! They sometimes make definite, testable predictions which are rarely wrong.

But enough bad jokes. Time to understand conditional probability. Suppose we want to understand how global warming will affect something of interest to an economist, say, Social Security funding. We first have to accept that global warming is true, as we did at the beginning.

We then have to accept a whole string of assumptions of how slight changes in temperature will affect Social Security. We might theorize that people will live slightly longer in warmer weather: consider all those folks who head to Florida once their odometers pass 65. And longer lives lead to greater burdens on the Social Security trust fund. Makes sense, right?

Since this year is the first in which outlays are greater than income into that fund, people are living longer, and Lederman claims global warming is worse than we knew, this is certainly a plausible theory.

With theory in hand, the economist can then use probability to estimate the likelihood of various Social Security budgets, given the belief that global warming is real and given the string of assumptions tying Social Security to climate are true. This is a conditional probability estimate, and the conditions are those “givens” just mentioned.

He can then use the conclusion that, once global warming hits, higher payouts are likely. And after becoming bothered about this, he’ll sign a letter to the Senate.

The point is obvious: because an economist’s model says “Social Security in trouble because of global warming” it is not true that Social Security is in trouble because of global warming. It might not even be likely to be true.

Conditional probability works in a nested fashion. In order to fully quantify the probability of a theory, we have to string out the list of conditional statements, assigning separate probabilities to each assumption as we go along, and multiply them all together. Since probabilities of these kinds of events are numbers between zero and one, multiplying strings of probabilities together results in a much smaller chance the entire theory is true.

Even the IPCC says there is only a 90% chance that global warming is true. That’s our first conditional. Then we have to assign probabilities to the other assumptions in the economist’s model. People living longer will lead to higher Social Security payouts is true, but only conditional on payouts remaining at their same levels. And what’s the probability of that? Not high, even given no effect of climate of length of life. Say it’s only 40%.

With just these two estimates, using our multiplying rule, we have 90% x 40% = 36%. That’s the upper limit on the truth of the economist’s theory. And we haven’t even considered his other assumptions, all of which lower the probability that Social Security will be affected by global warming.

Most studies which claim “X will happen given global warming strikes” are in this boat. They all are subject to the rules of conditional probability. And the likelihood they are true are almost always overstated because the theory holders never multiply their probabilities out like we have.

This is much too complicated for one post: Next time, we’ll sort this all out.

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