One ought to be ashamed to make use of the wonders of science embodied in a radio set, the while appreciating them as little as a cow appreciates the botanic marvels in the plants she munches.

Source: The Science of Radio by Paul Nahin, first edition

I’ll tell you what I see. I see some kind of vague showy, wiggling lines  — here and there an E and a B written on them somehow, and perhaps some of the lines have arrows on them — an arrow here or there which disappears when I look too closely at it. When I talk about the fields swishing through space, I have a terrible confusion between the symbols I use to describe the objects and the objects themselves. I cannot really make a picture that is even nearly like the true waves. So if you have difficulty making such a picture, you should not be worried that your difficulty is unusual.

From The Feynman Lectures on Physics, volume II.

Other Feynman posts:

Richard Feynman and Captain Picard try to prove Fermat’s Last Theorem
Most important event of the 19th century
God is in the details

From TED

I’m starting by reading the Feynman lectures on E&M. After that I plan to read something on electronics. If you have resources you recommend, please let me know.

I’ve started new Twitter account, @GrokEM. I figure that tweeting about E&M will help me stick to my goal. My other Twitter accounts post on a regular schedule (plus a few extras) and are scheduled weeks in advance. GrokEM will be more erratic, at least for now. (In case you’re not familiar with grok, it’s a slang for knowing something thoroughly and intuitively.)

Here’s what Feynman said about mathematicians learning physics, particularly E&M.

Mathematicians, or people who have very mathematical minds, are often led astray when “studying” physics because they loose sight of the physics. They say: “Look, these differential equations — the Maxwell equations — are all there is to electrodynamics … if I understand them mathematically inside out, I will understand the physics inside out.” Only it doesn’t work that way. … They fail because the actual physical situations in the real world are so complicated that it is necessary to have a much broader understanding of the equations. … A physical understanding is a completely unmathematical, imprecise, and inexact thing, but absolutely necessary for a physicist.

Heinlein coined grok around the same time that Feynman made the above remarks. Otherwise, Feynman might have said that only studying differential equations is not the way to grok electrodynamics.

Related posts:

What it means to understand an equation
Most important event of the 19th century

Avogadro’s number may be an example of Stigler’s law, depending on your perspective. An episode of Engines of our Ingenuity on Josef Loschmidt explains.

The Italian, Romano Amadeo Carlo Avogadro, had suggested [in 1811] that all gases have the same number of molecules in a given volume. Loschmidt figured out [in 1865] how many molecules that would be.

You could argue that Avogadro’s constant should be named after Loschmidt, and some use the symbol L for the constant in honor of Loschmidt. Jean Perrin came up with more accurate estimates and proposed in 1909 that the constant should be named after Avogadro. Loschmidt made several important contributions to science that are now known by other’s names.

As I’d mentioned in an earlier post, there are some fun coincidences with Avogadro’s number.

1. N_A is approximately 24! (i.e., 24 factorial.)
2. The mass of the earth is approximately 10 N_A kilograms.
3. The number of stars in the observable universe is 0.5 N_A.

Taubes uses the phrase to refer specifically to epidemiology, though it applies to all science. He credits “a Scottish researcher” with coining the phrase, but doesn’t say any more about who this researcher was.

Related post:

Amputating reality

How can you tell when an expert is exaggerating? His lips move.

Some people will misunderstand his point. Roberts is not saying experts exaggerate their conclusions per se. He’s saying experts exaggerate their confidence in their conclusions.

If an expert says that playing a harmonica decreases your risk of influenza by 10%, she’s probably not making that figure up out of thin air (though I am). There probably was some data that implied the 10% figure. It’s not that the data suggested 5% and the scientist said “Let’s call it 10%.” But the quality and quantity of the data may not justify rushing out to buy a harmonica.

One reason experts exaggerate their confidence is that they may be at a loss for words to explain their degree of uncertainty to a popular audience. Journalists can understand “Harmonica playing is good for you” though they probably cannot understand confidence intervals, Bayes factors, or the differences between retrospective versus prospective experiments. (The experts may not really unstand these things either.)

Another reason for exaggeration is that you don’t get the attention of the press by making tentative claims. This creates an incentive to suppress uncertainty. But even if experts were transparent regarding their uncertainty, there would still be a selection bias: experts who are sincerely more confident are more likely to be heard.

There are numerous other reasons experts may be wrong, some psychological and some statistical.

I liked the first comment on Roberts’ post:

I tended to rate my colleagues partly by how often the words “I don’t know” passed they lips. Often = good.

Related posts:

Three reasons expert predictions are often wrong
The most powerful people are right
Most published research results are false

My guess is that cartoons may help keep your attention if you’re moderately interested in a subject. If you’re fascinated by something, cartoons get in the way. And if you’re not interested at all, cartoons don’t help. The cartoons may help in the sweet spot in between.

No Starch Press has given me review copies of several of their Manga Guide books. The first three were guides to the universe, physics, and relativity. I’ve reviewed these here and here. Recently they sent a copy of the newest book in the series, The Manga Guide to Biochemistry.

I’m much more interested in physics than biology, so I thought this would be a good test: Would a manga book make it more interesting to read about something I’m not very interested in studying? Apparently not. It didn’t seem that the entertaining format created much of an on-ramp to unfamiliar material.

It seemed like the information density of the book was erratic. Material I was familiar with was discussed in light dialog, then came a slab of chemical equations. Reading the book felt like having a casual conversation with a lawyer who periodically interrupts and asks you to read a contract.

Someone more interested in biochemistry would probably enjoy the book. Please understand that the title of this post refers to the fact that I find biochemistry uninteresting, not the book. If I had to study a biochemistry book, the Manga Guide to Biochemistry might be my first choice. At times I’ve found biochemistry interesting in small doses, describing a specific problem. But it would be nearly impossible for me to read a book on it cover to cover.

O’Reilly’s “Head First” series is similar to the Manga guide series, though the former has more content and less entertainment. I enjoyed the first Head First book I read, Head First HTML with XHTML & CSS. Maybe I enjoyed it because the subject matter was in the sweet spot, a topic I was moderately interested in. The cartoons and humor helped me stick with a dry subject.

When I tried another Head First book, I was hoping for more that same push to keep going through tedious content. The books clearly had the same template though with different content. What was interesting the first time was annoying the second time, like hearing someone tell a joke you just heard. So at least for me, the Head First gimmick lost some of its effectiveness after the first book.

Watch on TED.com

I recently learned that Fourier had a personal problem with heat.

Even though Fourier conducted foundational work on heat transfer, he was never good at regulating his own heat. He was always so cold, even in the summer, that he wore several large overcoats.

Source: The Physics Book

Here are a few fun coincidences with Avogadro’s number.

1. N_A is approximately 24! (i.e., 24 factorial.)
2. The mass of the earth is approximately 10 N_A kilograms.
3. The number of stars in the observable universe is 0.5 N_A.

The first observation comes from here. I forget where I first heard the second. The third comes from Andrew Dalke in the comments below, verified by WolframAlpha.

For more constants that approximately equal factorials, see the next post.

Related posts:

Pi seconds is one nanocentury
There isn’t a googol of anything
Simpler version of Stirling’s approximation
What is the shape of the Earth?

If you ignore data, the answer to every hard question is the same: the most powerful people are right. That way lies stagnation (problems build up unsolved because powerful people prefer the status quo) and collapse (when the problems become overwhelming).

Science is far more political than I had imagined before starting a career in science. Data trumps politics eventually, but it may take a long time.

This building contains chemicals, including tobacco smoke, known to the State of California to cause cancer, birth defects, or other reproductive harm.

I saw similar signs elsewhere during my visit to California, though without the tobacco phrase.

The most amusing part of the sign to me was “known to the State of California.” In other words, the jury may still be out elsewhere, but the State of California knows what does and does not cause cancer, birth defects, and other reproductive harm.

Now this sign was not on the front of the hotel. You’d think that if the State of California knew that I faced certain and grievous harm from entering this hotel, they might have required the sign to be prominently displayed at the entrance. Instead, the sign was an afterthought, inconspicuously posted outside a restroom. “By the way, staying here will give you cancer and curse your offspring. Have a nice day.”

As far as the building containing tobacco smoke, you couldn’t prove it by me. I had a non-smoking room. I never saw anyone smoke in the common areas and assumed smoking was not allowed. But perhaps someone had once smoked in the hotel and therefore the public should be warned.

Related post:

Smoking

As an example, suppose you have data on how a drug acts in monkeys and you want to infer how the drug acts in humans. There are two sources of uncertainty:

1. How well do we really know the effects in monkeys?
2. How well do these results translate to humans?

The former can be quantified, and so we focus on that, but the latter may be more important. There’s a strong temptation to believe that big data regarding one situation tells us more than it does about an analogous situation.

I’ve seen people reason as follows. We don’t really know how results translate from monkeys to humans (or from one chemical to a related chemical, from one market to an analogous market, etc.). We have a moderate amount of data on monkeys and we’ll decimate it and use that as if it were human data, say in order to come up with a prior distribution.

Down-weighting by a fixed ratio, such as 10 to 1, is misleading. If you had 10x as much data on monkeys, would you as much about effects in humans as if the original smaller data set were collected on people? What if you suddenly had “big data” involving every monkey on the planet. More data on monkeys drives down your uncertainty about monkeys, but does nothing to lower your uncertainty regarding how monkey results translate to humans.

At some point, more data about analogous cases reaches diminishing return and you can’t go further without data about what you really want to know. Collecting more and more data about how a drug works in adults won’t help you learn how it works in children. At some point, you need to treat children. Terabytes of analogous data may not be as valuable as kilobytes of highly relevant data.

Related posts:

Big data is not enough
Does gaining weight make you taller?

The story started getting wide-spread attention last summer when the Cancer Letter reported that Dr. Potti had lied on grant applications. Since then there have been articles in the popular press, and people are staring to file lawsuits.

Apparently the tipping point in the story was finding a fib on Potti’s resume. According to The Economist

He falsely claimed to have been a Rhodes Scholar in Australia (a curious claim in any case, since Rhodes scholars only attend Oxford University).

So what finally got people to pay attention was not accusations of incompetent or fraudulent science, but résumé padding. As Keith Baggerly commented,

I find it ironic that we have been yelling for three years about the science, which has the potential to be very damaging to patients, but that was not what has started things rolling.

Related posts:

Popular research areas produce more false results
Using Photoshop on research results
Highlights from Reproducible Ideas

Scientists see their software as a kind of exoskeleton, an extension of themselves. Think Dr. Octopus. The software may do heavy lifting, but the scientists remain actively involved in its use. The software is a tool, not a self-contained product.

Programmers see their software as something they will hand over to someone else, more like building a robot than an exoskeleton. Programmers believe it’s their job to encapsulate intelligence in software. If users have to depend on programmers after the software is written, the programmers didn’t finish their job.

I work with scientists and programmers, often bridging the gaps between the two cultures. One point of tension is defining when a project is done. To a scientist, the software is done when they get what they want out of it, such as a table of numbers for a paper. Professional programmers give more thought to reproducibility, maintainability, and correctness. Scientists think programmers are anal retentive. Programmers think scientists are cowboys.

Programmers need to understand that sometimes a program really only needs to run once, on one set of input, with expert supervision. Scientists need to understand that prototype code may need a complete rewrite before it can be used in production.

The real tension comes when a piece of research software is suddenly expected to be ready for production. The scientist will say “the code has already been written” and can’t imagine it would take much work, if any, to prepare the software for its new responsibilities. They don’t understand how hard it is for an engineer to turn an exoskeleton into a self-sufficient robot.

Related posts:

Good, fast, cheap: Can you really pick two?
Buggy code is biased code

The US bank bailout exceeded the half-century lifetime budget of NASA.

Source

Other NASA-related posts:

After two days, I’d turned into an idiot
Good enough for Google and NASA
Team Moon

The Manga Guide to Physics basically covers force, momentum, and energy. The pace is leisurely. There’s not much back story; it cuts to the chase fairly quickly.This guide will not prepare you to solve physics problems, but it does give you a good overview of the basics.

(These books are not entirely manga; all three books I’ve seen in the series have several pages of more traditional textbook content.)

The Manga Guide to the Universe gives a tour of cosmology from the geocentric view to current theories. It contains some very recent material, such as references to the WMAP project.

This book is more rushed than the physics guide. That’s to be expected considering its ambitious scope. It devotes a fairly large amount of space to the back story and this contributes to the book being rushed.

I mentioned in my review of The Manga Guide to Relativity that although Americans associate cartoons with children, that book was not written for children. The physics guide, however, would be appropriate for a wide range of readers. Young readers may not fully appreciate the content, but they would not find anything offensive.

The Manga Guide to the Universe is inoffensive with one exception. There are a couple provocative frames in the prologue that will keep the book off some school library shelves.

I understand what an equation means if I have a way of figuring out the characteristics of its solution without actually solving it.

From a long view of the history of mankind — seen from, say, ten thousand years from now — there can be little doubt that the most significant event of the 19th century will be judged as Maxwell’s discovery of the laws of electrodynamics. The American Civil War will pale into provincial insignificance in comparison with this important scientific event of the same decade.

From The Feynman Lectures on Physics, Volume 2.

Related post:

Grand unified theory of 19th century math

I assume the intended audience, at least for the original Japanese edition, is familiar with manga and wants to learn about relativity. I came from the opposite perspective, more familiar with relativity than manga, so I paid more attention to the background than the foreground. My experience was more like reading The Relativity Guide to Manga.

I expected The Manga Guide to Relativity to be something like The Cartoon Guide to Genetics. However, the former has much less scientific content than the latter. A fair amount of the relativity book is background story, and the substantial parts are repetitive. As I recall, the genetics book was much more dense with information, though presented humorously.

Some parents and teachers will buy The Manga Guide to Relativity to introduce children to science in an entertaining genre. These folks may be surprised to discover the sexual undertones in the book. Americans typically equate comics with children, but the book was originally written for a Japanese audience that does not have the same view.

This has always been the main credo of my professional life. I have always tried to develop theories that shed light on the practical things I do, and I’ve always tried to do a variety of practical things so that I have a better chance of discovering rich and interesting theories. It seems to me that my chosen field, computer science — information processing — is a field where theory and practice come together more than in any other discipline, because of the nature of computing machines. …

History teaches us that the greatest mathematicians of past centuries combined theory and practice in their own careers. …

The best theory is inspired by practice. The best practice is inspired by theory.

Taken from Selected Papers on Computer Science.

Related post:

Works in the field, not in the lab
Works well versus well understood

Some of the deadliest viruses in the biological books — from influenza to herpes, from polio to the AIDS virus — are constructed using the shape of an icosahedron.

An icosahedron is a regular solid with 20 triangular faces.

In case you’re curious, here’s how I make the icosahedron image.

The book is exactly what you might expect from the title: a quirky little book about squid. I didn’t particularly enjoy it, but that’s my fault. I just wasn’t as interested in reading a quirky little book about squid as I thought I would when the publisher offered me a copy. Squid are bizarre creatures, and some other time I might enjoy reading more about them.

The title is terrific. I probably wouldn’t have given the book a second thought if it had been entitled, for example, Teuthology. And the title isn’t just sensational; squid really are curious, possibly exciting, and at least slightly disturbing.

The biggest surprise is how much time I have to spend getting funding for my research. Although it varies a lot, I guess that I spent about 40% of my time chasing after funding, either directly (writing grant proposals) or indirectly (visiting companies, giving talks, building relationships). It is a huge investment of time that does not always contribute directly to your research agenda — just something you have to do to keep the wheels turning.

According to this Scientific American editorial, 40% is typical.

Most scientists finance their laboratories (and often even their own salaries) by applying to government agencies and private foundations for grants. The process has become a major time sink. In 2007 a U.S. government study found that university faculty members spend about 40 percent of their research time navigating the bureaucratic labyrinth, and the situation is no better in Europe.

Not only do scientists on average spend a large amount of time pursuing grants, they tend to spend more time on grants as their career advances. (This has an element of tautology: you advance your career in part by obtaining grants, so the most successful are the ones who have secured the most grant money.)

By the time scientists are famous, they may no longer spend much time actually doing science. They may spend nearly all their research time chasing grants either directly or, as Matt Welsh describes, indirectly by traveling, speaking, and schmoozing.

Absence of evidence is not evidence of absence.

It’s the kind of thing a Sherlock Holmes-like character might say in a detective novel. The idea is that we can’t be sure something doesn’t exist just because we haven’t seen it yet.

What bothers me is that the statement misuses the word “evidence.” The statement would be correct if we substituted “proof” for “evidence.” We can’t conclude with absolute certainty that something doesn’t exist just because we haven’t yet proved that it does. But evidence is not the same as proof.

Why do we believe that dodo birds are extinct? Because no one has seen one in three centuries. That is, there is an absence of evidence that they exist. That is tantamount to evidence that they do not exist. It’s logically possible that a dodo bird is alive and well somewhere, but there is overwhelming evidence to suggest this is not the case.

Evidence can lead to the wrong conclusion. Why did scientists believe that the coelacanth was extinct? Because no one had seen one except in fossils. The species was believed to have gone extinct 65 million years ago. But in 1938 a fisherman caught one. Absence of evidence is not proof of absence.

Though it is not proof, absence of evidence is unusually strong evidence due to subtle statistical result. Compare the following two scenarios.

Scenario 1: You’ve sequenced the DNA of a large number prostate tumors and found that not one had a particular genetic mutation. How confident can you be that prostate tumors never have this mutation?

Scenario 2: You’ve found that 40% of prostate tumors in your sample have a particular mutation. How confident can you be that 40% of all prostate tumors have this mutation?

It turns out you can have more confidence in the first scenario than the second. If you’ve tested N subjects and not found the mutation, the length of your confidence interval around zero is proportional to N. But if you’ve tested N subjects and found the mutation in 40% of subjects, the length of your confidence interval around 0.40 is proportional to √N. So, for example, if N = 10,000 then the former interval has length on the order of 1/10,000 while the latter interval has length on the order of 1/100. This is known as the rule of three. You can find both a frequentist and a Bayesian justification of the rule here.

Absence of evidence is unusually strong evidence that something is at least rare, though it’s not proof. Sometimes you catch a coelacanth.

Related posts:

Estimating the chances of something that hasn’t happened
Complementary validation

It was June 24, 1835 at a meeting of the British Association for the Advancement of Science. Romantic poet Samuel Taylor Coleridge was in attendance. (He had previously written about the scientific method.) Coleridge declared that although he was a true philosopher, the term philosopher should not be applied to the association’s members. William Whewell responded by coining the word scientist on the spot. He suggested

by analogy with artist, we may form scientist.

Since those who practice art are called artists, those who practice science should be called scientists.

This story is comes from the prologue of Laura Snyder’s new book The Philosophical Breakfast Club. The subtitle is “Four Remarkable Friends Who Transformed Science and Changed the World.” William Whewell was one of these four friends. The others were John Herschel, Richard Jones, and Charles Babbage.

Update 1: Will Fitzgerald created the following Google Books ngram that suggests that scientist was used occasionally before 1835 and would take another 30 years to start being widely used in books. Click on the image to visit the original ngram.

So it is with many innovations: the person credited with the innovation may not have been entirely original or immediately successful. Still, perhaps Whewell’s public confrontation with Coleridge gave scientist a push on the road to acceptance.

Update 2: Pat Ballew fills in more of the story on his blog including editorial opposition to the term scientist. Pat brings more famous people into the story, including H. L. Mencken, Michael Faraday, and William Cullen Bryant.

Update 3: Here’s an excerpt from The Philosophical Breakfast Club.

More 19th century science:

Why Mr. Scott is Scottish
Victorian method for predicting height
Grand unified theory of 19th century math

Hard-edged science still dominates public perceptions, but the attention of modern scientists has swung heavily toward rainfall-like subjects, the kind where random behavior plays a major role. … Deterministic Newtonian science is majestic, and the basis of modern science too, but a few hundred years of it pretty much exhausted nature’s storehouse of precisely predictable events. Subjects like biology, medicine, and economics require a more flexible scientific world view, the kind we statisticians are trained to understand.

Certainly there is increased interest in systems containing “a heavy dose of randomness” but can we really say that we have “pretty much exhausted nature’s storehouse of precisely predictable effects”?

Source: Modern Science and the Bayesian-Frequentist Controversy

Related posts:

Scientific results fading over time
Occam’s razor and Bayes’ theorem
The law of medium numbers

JC: So how fast is the ball going when it hits the ground?

RC: Zero. It stops before it bounces back up.

JC: Well, how fast is it going just before it hits the ground?

RC: They didn’t ask the almost final velocity. They asked for the final velocity.