It's kinda hard to read 'cause all the lines are red

I'd say that in the final takeaway charts the blue lines are broadly consistent with the red lines. It's hard to say which red lines 'cause all the lines are red.

Harris: This green line represents our product. And the other green lines represents the competitors' product. So what we've got here is basically a case of ... 

Don: Uh-oh. See, it's kinda hard to read 'cause all the lines are green.

Reaching for the relatable application

These kids are totally rad; all I managed to do at their age was break a Raman spectrometer. However, I think the desire to say Khare's discovery could be used for cell phones is a bit of stretch. Here is the relevant diagram:

You'd need a supercapacitor ~10 times the size of your current cell phone battery to carry as much energy and you'd need to dissipate a lot of heat to have it discharge more slowly.

Is X better than Y if X is perfectly correlated with Y?

The Dow Jones Industrial Average hit its first record in a few years the other day and I was bombarded with mathematical innumeracy stampeding out of the speakers of my car like a herd of ... well, innumerate journalists.

First came this from NPR:

DAVIDSON: Here's the thing. For reasons I cannot understand, nobody adjusts for inflation when they're talking about the Dow. .... And anyway, even if it did reach a record, this is not the measure we should be paying attention to. 

BLOCK: OK. Well, if it's not the measure we should be paying attention to, what is? 

DAVIDSON: There are, as I mentioned, a handful of indexes that do a much better job, like the S&P 500 ...

Don't worry too much right now about the fact that the S&P 500 isn't adjusted for inflation either. More came from Marketplace minutes later:

“[The Dow] a rough indicator of the health of the market,” says Kelly School of Business professor Scott Smart, “but there are some problems with the Dow as such an indicator." 

For one, the Dow is a very, very small sample ... “since it only looks at 30 stocks, there are obviously big portions of the market that the Dow doesn't monitor or doesn't capture.” ... 

... “it is weighted in a very unusual way.” ... 

That’s the Dow. No complicated formula. No algorithms. No wonder more serious investors prefer the S&P 500 ...

So while the Dow is a giant turd on the world of financial journalism, the S&P 500 is, like, totally the awesomest.

Except they have a correlation of 0.96 ...

The Dow is in blue and the S&P 500 is in gray. Or, wait ...

Here is another view. Basically Dow = a*S&P 500 (where a is ~ 9.2) ... and you will only be off by a couple percent.

The simplicity of the formula, its "unusual" weighting based on price not market cap, its small sample size: none of these things matter. Why? Because most companies large enough to be listed in an index are highly correlated with each other. Here, for example, are Boeing and GE:

It doesn't matter how you weight the companies since these weights have no effect on correlation  ... if the correlation of X and Y is c, then the correlation of a*X and b*Y is also c.

And even if individual companies weren't very correlated with each other, creating indices that lump individual companies together tends to destroy the information about their individual performance so you end up with an index that shows an average trend (this is the idea behind these indices in the first place). All that matters is how highly correlated the stocks are in the first place whether it takes 30 companies, 500 or 5000 to get there. (The answer is 30.)

Study changes (NYT editors') understanding of how DNA causes disease

Study Changes Understanding of How DNA Causes Disease

By GINA KOLATA 1:03 PM ET

At least four million gene switches that reside in bits of DNA once thought to be inactive turn out to play critical roles in health, researchers reported.

http://www.nytimes.com/2012/09/06/science/far-from-junk-dna-dark-matter-proves-crucial-to-health.html

So, what is your first take on what this story is about? Just, say, reading the title and the lede. It sounds like this is some sort of new result about how "junk DNA" actually does something. Wow! And there might be new understanding of (potentially all?) disease! 

Of course, these pieces of junk DNA had been known to be associated with certain diseases for a decade. In fact, the author likely knew this as it is written in the article.

In large studies over the past decade, scientists found that minor changes in human DNA sequences increase the risk that a person will get those diseases.

The earliest papers are from the late 1990s and 2000s. As the Human Genome project was coming up with much less than it expected, scientists pushed into this area. 

And of course, gene switches aren't new -- another fact the author likely knew as it too is written in the article.

In recent years, some [scientists] began to find switches in the 99 percent of human DNA that is not genes

I think the author left off what recent years meant because 10 years doesn't sound so new. In recent years (2007) it was sufficiently established for NOVA to cover it.

http://www.pbs.org/wgbh/nova/body/gene-switches.html

In fact, the entire concept has been around for awhile. The reason I wrote this particular post is that I personally have known about this simply through the aforementioned NOVA episode. I knew enough about gene switches in 2008 to comment (with proto-spittle flecked ire) on an idiotic statement by Ray Kurzweil saying the brain is simple because a human DNA sequence consists of only "50 million bytes" of information. I said:

In the worst case, a sizable fraction of all 2^20000 [gene on/off] states could be involved to get from a stem cell to every neuron in its right place of the brain with the proper function.

http://tierneylab.blogs.nytimes.com/2008/06/10/when-do-post-humans-show-up/?apage=2#comment-17685

I don't want to detract from the actual work presented in the article. It is a pretty awesome piece of human genome mapping, and it really sheds light on how complex the whole thing is.  (And it puts some more hurt on Kurzweil since the entire 3D structure along with the switches appears to be important in DNA.) 

Gina Kolata seems to be a stand-up molecular biologist cum journalist. I imagine the editors of the NYT were completely blown away by progress in stuff they hadn't been paying attention to since the 1990s (or maybe ever) and said she should change the lede. 

And I guess it got me to click the link.

Other than the grammar ...

How often do people ask the question Other than the grammar, how was the speech? Well, apparently over 298 million times. 

Human speech derives its information carrying capacity from several places not the least of which is its temporal structure. Sorting on word frequency literally destroys significant quantities of information. The entire information content of the word green next to the word frog (i.e. green frog for those following along at home) is that green is modifying frog so as both to convey the information that the frog is green and distinguish said frog from e.g. a poison dart frog (which is not green, but instead blue or yellow). If I take that word green and move it to different position unrelated to the position of frog, that word green no longer carries any information at all ... and any information you do decide to imbue it with has no foundation whatsoever.

The above word cloud (apparently also known as a wordle, though that just may be specific generation software) of Romney's speech to the RNC has removed all of the information except that he might be running for President of America. However that is information I am adding to this infographic. The speaker simply mentions President and America. It could be in a negative light. The most commonly appearing words in this blog post are green and frog, but I'm not talking about green frogs. In a sense, the creators have only done a half-assed job. Below I lay waste to the information content, reducing the speech to an empirical estimate of the letter frequency in English.

(Sorry. I couldn't help myself.)

Fun with normalization, economics edition

So the new thing in economic circles is for rich countries to look at poorer ones as economies to emulate and this has sparked some kind of debate about Iceland, Estonia, Latvia, Ireland and Lithuania: which one fared best during the recession? And debate begets ... graphs! I love graphs.

This blog summarizes the graphs, but takes a demonstrably wrong view of the data. I link to it because it and the side it supports are to be the recipients of my spittle-flecked ire.

The subject of the graphs are the RGDP data for the aforementioned countries. Now RGDPs of all countries at any particular time form a power law distribution, making it difficult to graph on standard axes in a way that conveys information. That's why humans in all their wisdom have invented several ways to "enhance" graphical information to try and make their point. Percent changes, derivatives, normalization, logarithmic scales: pick your poison. But make sure you pick the right poison because certain poisons work on certain subjects.

Let's start with the "raw" RGDP data. Iceland is in blue, the rest red (because the question is: Is Iceland faring better?). I forgot to put units on the graph (bad Bourbaki) but the y-axis is Millions of 2005 Euros.

Iceland has only a few hundred thousand people in it so its RGDP is pretty small. However, RGDP per capita is huge; Iceland and Ireland are wealthy countries relative to the others. So according to one metric, How much money do you have?, Iceland wins with a much larger RGDP per capita.

But we want to look at the recession, so one side of this "debate" made the choice to normalize to the pre-recession peak. This is standard practice in economics. When looking at a recession, it only lasts a few years so inside of that window your RGDP data are approximately linear. Normal growth rates (r) are on the order of a few to several percent per year (t) so r*t << 1 for several years. For linear data, normalization is fine, but you need a way to select your normalization point that isn't arbitrary ... hence choosing the peak (or other feature). This is what we get.

You can see Iceland near the top from 2008 to 2012 since its recession wasn't as big relative to the peak. Even the pre-recession data has some value because it shows the run-up to the peak (slope) was shallower in Iceland. Lots of information. The pre-peak levels are not valid for points far from the peak for reasons we'll describe later. Overall, lots of information. Excellent.

Except that the libertarians of the world love the Baltic countries because one of them mentioned Milton Friedman at one point. So they set out to show this wasn't correct. They chose to normalize to the year 2000. And thus, Iceland sucks.

Why 2000? No idea. The peak of Iceland's boom was about 2002; the year 2000 also represented unremarkable years in the other countries. You can choose other years. In fact, if you choose other years, you can show Iceland being anywhere from the bottom to the middle of the pack (2003) ...

To the top of the heap (2006) ...

Actually, by choice of normalization year, you can show any country listed to be at the top of the heap during the recent recession (2008 to today). Iceland in 2007, Estonia in 1997, Latvia in 2011, Ireland in 2011, and Lithuania in 1997. In fact, the year 2000 is the year you'd choose if you wanted to show Iceland at the bottom (which makes me think this was deliberately manipulated by one side of the argument).

In general, a normalizing time series data that is linear in log space creates a time dependent scale. For short times, log(a+b*x) ~ log(a) + x*(b/a) + o(x^2). You can normalize lines. But over 10 years or so with growth rates on the order of a few to several percent per year, you need those o(x^2) terms.

If you look back to the first graph, you can see a nice long linear trend in the data, which suggests the correct way if you want to look at recovery from a deviation from the previous trend: fit the pre-crisis trend and look at the percent difference.

Here are some fits to the pre-crisis trend (Iceland: blue, Estonia: red, Latvia: orange) ...

Note these linear fits have different slopes and intercepts. That's why normalization to a specific year allows you to put any of the countries on top. Also note that the slopes are higher for the Baltics. I think this is what the libertarians are trying to give credit for, but the overall higher trend is not germane to the question of how bad the recession is. Additionally, all poor countries like the Baltics all have higher growth rates than rich countries like Iceland and Ireland. Rapid growth from a low base is what is behind massive growth numbers from China, for example. You can think of it as picking the low-hanging fruit. (China is in the process of transforming low productivity agricultural workers to higher productivity industrial workers.)

The result after taking the percent difference from these trends (Iceland: blue, everyone else: red) ...

Iceland is on top again.

What have we learned?

• All data can be manipulated. If someone shows you a graph in a certain format (removing the origin, normalizing to some arbitrary year), question their formatting choices.
• Corollary: Especially question when someone decides to change the format of data previously graphed data to opine or make a political/partisan/school of thought's point. It could even be just to get more page views.
• Normalize and scale to features of your data (peaks, troughs, trends), not arbitrary points.
• Specifically, Iceland seems to have fared better than the Baltic countries (and Ireland) in the recession when the data is normalized to the peak or fit to the trend. Iceland is also doing better when measured by RGDP per capita. As these (peak, level, trend) are the only features of a linear data set besides, say, the level of noise/seasonal variations we can with confidence say that Iceland is indeed doing much better when measured with RGDP.

Marginal Revolution has been a serious offender on this kind of manipulation in the first bullet. Or at least on spreading the offending graphs around. This graph shows the same shenanigans mentioned here. This graph basically shows the first graph at the top of the page and asks what's the big deal? The big deal is of course that graphing on a linear scale in this case exaggerates the level when the question is about the trend. They are entering Freakonomics territory. My opinion of this last graph is well known.

It slices *and* dices?

http://www.nytimes.com/2012/05/13/opinion/sunday/the-wisdom-of-slime.html

Problems solved by the slime mold include ... other complex mathematical challenges (like creating a Voronoi diagram and a Delaunay triangulation).

A Delaunay triangulation is a dual graph to a Voronoi diagram. They are the same mathematical problem (at least with the ordinary distance metric ... and guess what ... they didn't bother with any other metrics). It sounds neater when you put them both in as examples, though.

Nice to see that the NYT is coming around to stuff that was reported two years ago.

Blood from The Stone

The Stone is one of the greatest threats to intellectual discourse since the invention of the blog.

“I can’t answer ['What is philosophy?'] directly. I will tell you why I became a philosopher. I became a philosopher because I wanted to be able to talk about many, many things, ideally with knowledge, but sometimes not quite the amount of knowledge that I would need if I were to be a specialist in them. It allows you to be many different things. And plurality and complexity are very, very important to me.” (Alexander Nehamas)

Nehemas became a philosopher in order to further his desire to talk out of his ass. I think that about sums it up.

Luckily for us, there was an example of this from earlier in the month.

Take for example mathematics**, theoretical physics, psychology and economics***. These are predominately rational conceptual disciplines. That is, they are not chiefly reliant on empirical observation. For unlike science, they may be conducted while sitting in an armchair with eyes closed.

Ok, I'll bite. theoretical physics is not based on data.

As such, whereas science tends to alter and update its findings day to day through trial and error, logical deductions are timeless(**). This is why Einstein pompously called attempts to empirically confirm his special theory of relativity “the detail work.” 

Ha ha. Einstein is pompous. Wait; I thought you said theoretical physics is not based on empirical observation?

Indeed last September, The New York Times reported that scientists at the European Center for Nuclear Research (CERN) thought they had empirically disproved Einstein’s theory that nothing could travel faster than the speed of light, only to find their results could not be reproduced in follow-up experiments last month. Such experimental anomalies are confounding. But as CERN’s research director Sergio Bertolucci plainly put it, “This is how science works.”

So now theoretical physics is based on empirical data? I pause to note that these are two consecutive paragraphs.

I have two subsequent points.

• This would not have "disproved" Einstein's theory. It would have meant that Einstein's theory was some approximation to some underlying theory. GPS, which uses Einstein's General Relativity to work, would keep on working. Muons generated from cosmic rays would still make it through the atmosphere.
• It wasn't that their results couldn't be reproduced. There was an error that didn't take into account the difference in clock speeds at different points in the Earth's gravitational field of some kind. (Per Student, a loose cable.)

Talking out of one's ass indeed.

** "However, 5 plus 7 will always equal 12. No amount of further observation will change that." Except in modular arithmetic. Or any other redefinition of the binary operator "+". Or in different bases. Or adding 5 mL of isopropyl alcohol to 7 mL of water. The reason you can be sure of, snarking aside, the constancy of the underlying claim is that it is arbitrary. It is a small step from the Peano axioms to 5 + 7 = 12 and therefore it is as arbitrary as those axioms. Timeless, indeed. We see the fallacy of the preeminence of human thought again.

*** I don't think it was an intentional hit on economics, but I like it. They do make charts with empirical data in them. So does psychology.

You're killing me, NASA

The Kepler mission is doing pretty well. Recently they have discovered a couple of new planets. Fine. However, I'm pretty sure the only thing we actually know about these planets is that they are probably spherical. The artist conceptions take it a little too far. Why not go all the way?

I have a separate problem with the line-up. At first glance it looks like a bit more than a 3% difference in radius, but the south poles are all lined up for the other planets while Kepler-20f is bumped up a bit. I have no idea why they did this since I thought the similarity to the radius of the Earth was the interesting fact.

Charity for the straw man

This person is a professor of something. That is not to say that I don't know what he is a professor of -- public affairs and economics in fact -- but that he passed the GRE at some point (likely with pretty good scores), graduated from grad school, probably got a post-doc or associate professorship, and became tenured. And now he starts a blog post with two regressions with R^2 = 0.21 and R^2 = 0.09 with n < 10 and then concludes that it justifies something besides inconclusiveness. That is our standard beef here, but not my biggest beef today. I have a meta-beef.

The other professor (Sumner) being criticized by this post only made the claim that deviations from Okun's law were small, hence unemployment was basically explained by NGDP. Therefore showing that deviations from Okun's law are within the error of the model as Chinn does proves nothing. That is the original point. You do not show someone's point is flawed by demonstrating their point.

I will be (uncharacteristically) charitable now to get to the heart of the meta-beef. It could be that Chinn is trying claim the model does not work better with revised data. I'll call this the uncharitable form of Sumner's claim. However, the original curve inside the error of the model; it is impossible to work better. Chinn is setting Sumner up to fail by using the uncharitable form, but writes with the pretense that the uncharitable form could be correct --  that the uncharitable from is decidable given the data, and therefore you can set out to demonstrate it in an unbiased, scientific manner.

Of course, writing with the pretense that uncharitable form is decidable you'd actually have to allow that it is possible the model works worse with the revised data. That means you would have to consider the charitable form of Sumner's claim: given the revised data the model still works just as well. Since GDP revisions tend to get smaller over time, Sumner can say now that the revisions are in, we still have the same picture. That while the GDP vs employment data could have gotten revised to deviate from the model, it did not; that is Sumner's "final nail in the coffin". No one will likely ever produce data that will show that Sumner was wrong.

I know, I know: uncharitable representation is an age-old device in science. But given an uncharitable representation of Simplicio's Aristotelian position, Galileo's arguments in favor of Salviati's Copernican position did not imply a charitable representation of the Aristotelian position because both sides did not believe in the same underlying model. The underlying model was the debate. Here, Chinn and Sumner are both using the same model. The conclusions are the debate.

Fauci and McNeil: continuing adventures in bad science

WTF?!! [spit] AAAAAAAAAHHHHHH!!! [spit] #$%&!!!!!

What pharmaceutical company is paying to plant these articles?

This has the added outrage of not just being terribly unscientific but also morally suspect methodology. Not only is sqrt(1763/2) ~ 30, but they stopped the trial early when the difference started to appear significant. I can prove any coin is unfair if I stop flipping it when it starts to appear unfair.

Dr. Fauci and Mr. McNeil were apparently at it yet again ... we've caught them before:

http://spittlefleckedire.blogspot.com/2010/05/origins.html

http://spittlefleckedire.blogspot.com/2010/12/yes-this-again.html

Mr. McNeil also has this recent article. There is a theme involving the University of North Carolina, but they do have a center for AIDS research there. However, "Pharmaceutical Product Development, Inc." was a major donor to local races in North Carolina last year, though I haven't found any particular connection to any of the drugs in the trials or a ton of references to AIDS on their website. They have provided "research management" to NIH/NIAID (Dr. Fauci's organization) specifically for HIV/AIDS research. All of this could be plain "synergy" and the poor scientific methodology plain confirmation bias. I don't think there has to be something nefarious going on here.

I wonder if there isn't a deeper connection to this article. Pure speculation on my part at this point, but a couple of my favorite quotes ...

"We've never done ghostwriting, per se, as I'd define it", says John Romankiewicz, president of Scientific Therapeutics Information, the New Jersey firm that helped Merck promote Vioxx with a series of positive articles in medical journals. "We may have written a paper, but the people we work with have to have some input and approve it."

Underlining mine. And ...

Alastair Matheson is a British medical writer who has worked extensively for medical communication agencies. He dismisses the planners' claims to having reformed as "bullshit".

Spittle-flecked ire, indeed. Fauci and McNeil may be the worst collaboration of a scientist and a journalist since Levitt* and Dubner.

*I'm using scientist in loose sense here. Levitt is an economist, but a lot of economists use the scientific method. It is even looser because I'm pointing out flaws in the use of the scientific method.

We have another contender for the worst graph ever made

Or should I say three? I don't even think I need to say anything.

The reigning champ has the added benefit of using more complicated methods in the disservice of the pursuit of knowledge, but the new one has the added spice of moral outrage of poor scholarship in the service of the elite.

Editor to Overbye: We need another article about the dark matter.

This is a horrible way to do science.

Republicans and unions are natural allies. Sort of.

Against Moore

So I was reading this, and then this, and then this, etc. Having spent most of my academic career (and now career) in male-dominated fields, I'm well aware of many of these gender issues. The problem is probably intractable until we reach a post-gender society where reproduction is either unnecessary or controlled by the state.

My issue is with philosophy. (Still.) And particularly Moore:

... it would be better for a beautiful planet to exist than an ugly one even if there were no one around to see it ...

 Let's replace beautiful/ugly and better/worse with positive/negative (gets us away from that whole gender morass). It would be more positive for a positive planet to exist than a negative one even if there were no one around to see it. The first half is a tautology; the second half states the tautology is true (duh) regardless of the existence of a choice function and hence independent of the axiom of choice. But definitions are independent of the axiom of choice. That I am unable to select a planet from a set does not mean given a planet and a function that assigns each planet onto the set {-1, +1} I can't find its image in that space.

It would be interesting if Moore considered the consequences of the axiom of choice, whether we can see it or not does have an effect in mathematics (and quantum mechanics, depending on who you talk to). But usually weird ones, like the Banach-Tarski paradox.

And this:

It's raining but I don't believe that it is raining

Under most circumstances, I believe this means the speaker is surprised it is raining. It is an emotional utterance; it is not weird or nonsensical in any way. People do not contain within them fully consistent sets of heuristics.  Supposedly Wittgenstein considered this Moore's greatest contribution to philosophy. This is another example of the fallacy of the preeminence of human thought that philosophy falls for so often.

In the holiday spirit

Lest Spittle-flecked Ire become entirely cantankerous, I'd like to point out one of the best articles I've ever read in the NYT Science section: A Scientist, His Work and a Climate Reckoning.

Lest Spittle-flecked Ire lose it edge, I'd like to point out my only complaint is that when the article states

Later chemical tests, by Dr. Keeling and others, proved that the increase was due to the combustion of fossil fuels.

it does not mention which chemical tests. CO2 produced by burning fossil fuels will have much less Carbon-14 as it comes from sources much older than 6000 years, so the atmospheric ratio of Carbon-14 to Carbon-12 will steadily decrease while [CO2] increases.

http://en.wikipedia.org/wiki/Suess_effect

However, one must account for a countervailing production of Carbon-14 during the nuclear test era. And there is some neat stuff about relative Carbon-13 depletion involving evolving chemical processes in plants that I just learned about a few minutes ago.

It does help me see why Creationists and the "global warming is a hoax" crowd are natural allies.

My Rationally Held View of Humans

I borrowed this quote from here.

It appears, therefore, that a swarm's scout bees do something sharply different from what humans do to reach a full agreement in a debate.  Both bees and humans need a group's members to avoid stubbornly supporting their first view, but whereas we humans will usually (and sensibly) give up on a position only after we have learned of a better one, the bees will stop supporting a position automatically.

I do not think that is true at all. Humans rarely give up on their positions*, and are frequently selectively skeptical or easily convinced by data that either confronts or confirms their stubbornly supported first view. 

This is a good short survey pointing to a couple studies of how humans really behave that includes a great quote on this subject by Bertrand Russell:

If a man is offered a fact which goes against his instincts, he will scrutinize it closely, and unless the evidence is overwhelming, he will refuse to believe it. If, on the other hand, he is offered something which affords a reason for acting in accordance to his instincts, he will accept it even on the slightest evidence.

My own favorite quote on this is from Max Planck:

A new scientific truth does not triumph by convincing its opponents and making them see the light, but rather because its opponents eventually die, and a new generation grows up that is familiar with it.

You can just feel the ire.

* Positions, such as this human's view of how humans behave regarding their positions.

Yes, This Again.

The impetus behind this blog was a study that written about in the NYT about an AIDS vaccine. The study was completely inconclusive, yet was promoted as a major advance. Here we go again:

In the study, 2,499 men in six countries — Brazil, Ecuador, Peru, South Africa, Thailand and the United States — were randomly assigned to take either Truvada or a placebo and were followed for up to three years. For ethical reasons, they were also given condoms, treatment for venereal diseases and advice on safe sex. There were 64 infections in the placebo group and 36 in the group that took Truvada, a 44 percent risk reduction.

Two in the Truvada group turned out to have been infected before the study began. When the remaining 34 were tested, only 3 had any drug in their blood — suggesting that the other 31 had not taken their pills.

Different regimens, like taking the pills not daily but only when sex is anticipated, also need testing.

Also, many men in the study failed to take all of their pills, and some clearly lied about it. For example, some who claimed to take them 50 percent or 90 percent of the time had little or no drug in their bloodstreams.

The pills caused no major side effects, though men who began to show signs of liver problems were taken off them quickly. Some men stopped taking the pills because they disliked relatively minor side effects like nausea and headaches. Also, some stopped bothering once they suspected that they might be taking a placebo.

“People have their own reasons,” [Policy Director of amfAR Chris] Collins said. “People don’t take their Lipitor every day either.” 

Again, how hard is it to take sqrt(1250)? But then there's the feckless methodology! Truvada is also apparently effective regardless of the regimen. Imagine that. So is prayer.

I was wondering what is going on with these AIDS studies that keep showing comparably inconclusive results, yet promoting them as breakthroughs. So I went back and looked at the previous study. Then I found this released a month later calling into question that study. And then I realized: all three of these articles were written by the same author, Donald McNeil, and both studies were under funded via the National Institutes of Health infectious diseases director Dr. Anthony Fauci. But this was especially rich:

Putting several biostatistical analyses in a news release “would have confused everybody,” Dr. Fauci said, and suggesting that the researchers were engaging in a cover-up is “absurd.”

Everybody? Statistical analyses are not confusing if they are conclusive; if only one method out of several confirms your hypothesis, your data is not conclusive.

Glucose and Aggression ?

The major discovery is that there is a journal called Aggressive Behavior.

Science Daily has reported on a recent work that "Sweetened blood cools hot tempers." The claim is that people who drink lemonade sweetened with sugar show less aggression towards strangers within a short time window after the drink. The experiment is exquisite :

In the study, 62 college students fasted for three hours to reduce glucose instability. They were told they were going to participate in a taste-test study, and then have their reaction times evaluated in a computerized test against an opponent.

Half of the participants were given lemonade sweetened with sugar, while the others were given lemonade with a sugar substitute.

After waiting eight minutes to allow the glucose to be absorbed in their bloodstream, the participants took part in the reaction test.

The reaction test has been used and verified in other studies as a way to measure aggression. Participants were told they and an unseen partner would press a button as fast as possible in 25 trials, and whoever was slower would receive a blast of white noise through their headphones.

At the beginning of each trial, participants set the level of noise their partner would receive if they were slower. The noise was rated on a scale of 1 to 10 -- from 60 decibels to 105 decibels (about the same volume as a smoke alarm).

In actuality, each participant won 12 of the 25 trials (randomly determined).

Aggression was measured by the noise intensity participants chose on the first trial -- before they were provoked by their partner.

Results showed that participants who drank the lemonade sweetened with sugar behaved less aggressively than those who drank lemonade with a sugar substitute. Those who drank the sugar-sweetened beverage chose a noise level averaging 4.8 out of 10, while those with the sugar substitute averaged 6.06.

I can not dispute that a low blood sugar contributes to irritiability (and by extension, aggression.) Just last night, I waited too long between lunch and dinner and was becoming irritated with my surroundings until I was able to eat. But, I have just had a glass of extra-sweetened lemonade and I am still annoyed by the presentation of the study results. The comparison of the means of two populations is meaningless without an error bar on the mean. The numbers of members of each population is only 31, so the error on the mean may be significant relative to the difference of 1.26. Error bars please ! I don't want to pay to see the PDF of this article, so I am not sure if the fault lies with the authors or Science Daily. Did the authors measure their blood sugar? The article's abstract states :

Self-control consumes a lot of glucose in the brain, suggesting that low glucose and poor glucose metabolism are linked to aggression and violence.

Knowing this, it is imperative to know how well each participant metabolizes glucose. Probably more important would be to know the number of men and women in each population. The abstract goes on :

Study 1 found that participants who consumed a glucose beverage behaved less aggressively than did participants who consumed a placebo beverage. Study 2 found an indirect relationship between diabetes (a disorder marked by low glucose levels and poor glucose metabolism) and aggressiveness through low self-control. Study 3 found that states with high diabetes rates also had high violent crime rates. Study 4 found that countries with high rates of glucose-6-phosphate dehydrogenase deficiency (a metabolic disorder related to low glucose levels) also had higher killings rates, both war related and non-war related. All four studies suggest that a spoonful of sugar helps aggressive and violent behaviors go down.

Regarding Study 3 : I wonder what the relative levels of poverty are in these states.

Regress = Progress

In the generally annoying vein of journalists writing about science comes this. All of the items Mr. Horgan, apparent journalism school graduate, cites as regress are signs of progress. I think this points to a huge gulf between science and journalism. In journalism, realizing your proposed narrative is wrong means that you're back to where you started; in science, realizing your proposed narrative is wrong means you've made a giant leap forward in understanding.

Just think: how often do we see articles about, say, city government where the author has as its main point that  there is no narrative?

The key point is that frequently in science knowing more means we realize we know less.

I actually see one of Horgan's observations:

What I found fascinating was the issue's overall tone of caution rather than the traditional boosterish enthusiasm.

as progress in itself. "Boosterish enthusiasm" is, in my view, bad for scientific progress.

On to the examples ...

The end of infectious disease

We used to think this was a) possible, and b) if possible, economically feasible. We've come to realize that evolution can be more powerful than our technology.

Space colonization

This will probably still happen soon on the scale of the lifetime of the solar system with our currently main sequence star, so don't fret, Trekkies. But if you can tell me what the point of the ISS is beyond a giant subsidy to Boeing to maintain some of our space infrastructure, I will give you a thousand dollars. I have never seen a peer-reviewed paper come out of the ISS; nearly all of the science is done by unmanned probes. 

But the real problem I have is that colonization is not science. It being nearly Thanksgiving, a seasonal example: the Mayflower was not a scientific voyage. 

Supersonic transport

The science behind supersonic flight happened before 1947. The fact that it is not economical to send hundreds of people at a time to business meetings and/or tropical islands faster than the speed of sound, but is economical to send one person and thousands of pounds of bombs is not a scientific fact.

Commercial fusion power

This turned out to be much harder than we thought; I myself was discouraged by my advisors from pursuing this field back in the 1990s. However missing a 20 year guess at commercial viability 30 years ago does not mean the field is going backward. It is still moving very, very slowly forward. Additionally, arguments against fusion's commercial viability are themselves due to progress in the field. We understand the problem better, and therefore understand that it is not likely to be economical anytime soon.

The origin of life

Keep up with the research buddy. Most of the problems with Urey-Miller type experiments are due to the fact that we have no idea what the chemical composition of the Earth's atmosphere was at the time. This is another case where we know more now and realize we know less.