Nothing helps an argument like poorly assembled and decidedly misinterpreted statistics (when your opponent starts to point out your flawed reasoning, call him a Nazi). Still, Vali Chandrasekaran of Business Week thinks that we should all be careful drawing conclusions from merely correlated data points. There are several more humorous examples at the link.

Link -via @MarilynTerrell | Previously: The Science News Cycle

The world population is expected to reach seven billion within the next few days. Wonder what number you are? A calculator from the BBC helps you find out approximately where you fit in. I have a relatively low number, as there weren’t even three billion people when I was born. Link -via Breakfast Links

In the 1860s nearly all women managed to get hitched. Today, with a better gender ratio, only 22% of adults aged 18-29 are married and only 44.9% of adults in all adult age groups have ever been married. The median ages for first marriages have moved way up as well–from 23 for men and 20 for women in 1960 to 28 and 26, respectively, today. Divorce is hovering at the 50% rate.

‘Major attitudinal shifts’

Bolick notes that for women, marriage is now “an option rather than a necessity,” citing a dwindling pool of educated, committed men, a new majority of women in the workplace, a tanking economy, IVF and adoption, the rise of non-traditional families and marriage arrangements, and a dissipating “spinster” stigma.

Bolick represents the intentionally single thirty- or forty-something. The newest generation eschewing nuptials is the tech-savvy and generally liberal Millennial. With education leveling the playing field, opportunities to earn something beyond the MRS might just be higher on a girl’s list of priorities. Likewise, the responsibility of career, house and family (married or not) is what Sex at Dawn co-author Christopher Ryan calls “swimming upstream.” It’s perhaps inevitable that fewer women take it on.

Today’s women are professionally and financially more established, so they should be all that more appealing to males. They are, generally, but not in a “find The One and keep her” way. Men are also opting to remain single as long as they are happy. “If you have four quality women you’re dating and they’re in a rotation, who’s going to rush into a marriage?” asks Ralph Richard Banks, author of Is Marriage for White People? In response, Rod Dreher at the American Conservative lays it out: “Throw out traditional morality for an ethic of libertinism and you get men being what biology has programmed them to be. In this way, feminism, whatever its benefits for women, has hurt them.”

Changing expectations

Dreher’s insistence that being unmarried is a ‘hurt’ to the purposely single woman is debatable. But it’s clear that the expectations of marriage have changed rapidly over the last half-century. Women are not expected to be June Cleaver, and men are not expected to shoulder the full financial burden alone. And they can even cohabitate now without the nasty rumors that haunted earlier generations.

This doesn’t necessarily mean that healthy relationships are, or the desirability of the pair bond are declining. One could argue that without the legal constraints, the odds of finding a working, healthy relationship increase. Add to this the growing presence of nontraditional family groups (friends and extended family as family) and relatively commonplace single-parent household, and what you get is a less strict idea of what normal relationships are.

In Mexico City, in a move to counter high divorce rates, lawmakers have proposed a two-year marriage license. The trial-by-marriage would give newlyweds “an easy exit strategy” by allowing them to mutually decide whether or not to renew. Whether this is better than having never married at all is a completely different debate, but points out how marriage is not what it once was.

Tradition? Buck tradition.

Marriage as we know it is a relatively new concept. It wasn’t until marriage was used to procure and maintain land-ownership that the couple was limited in breaking that bond without permission. And when your husband or wife is chosen for their respective acreages, affection is an afterthought, if a thought at all.

And yes, a certain non-zero percentage of the population is still denied marriage by (most) state laws. Typically it’s argued that this denial of rights is to protect traditional marriage, but clearly marriage before the last century and since is not what we would call “traditional.”

Bolick’s article makes several major points that aren’t included here. But given that gender parity and economic downturn and the changing boundaries of social acceptance have come together to throw a wrench in our standard American Marriage, Bolick might be onto something.

Do you think that the declining marriage rates in the US are a problem, or is it just a shift in expectations from relationships and adulthood? Or, if you prefer: Is less marriage better, or worse?

Sources:

• All the Single Ladies
• The Atlantic‘s ‘Single Ladies’ cover story: 6 takeaways
• NBC Celebrates ‘End of Traditional Marriage,’ Perpetual Female Singlehood
• In U.S., Proportion Married at Lowest Recorded Levels
• Decline of men, decline of marriage
• ‘Til 2013 do us part? Mexico City mulls 2-year marriage

America’s taste for bean soup appears to be unrelenting, and the World Wide Web offers more than a quarter of a million references to the subject. Multiple-bean soups are particularly in vogue. A methodical check on a leading search engine produced the following results which I record here for future historians of early twenty-first century food. Unexpectedly, this research also thrown up food for thought for mathematicians.

The methodology for researching multiple-bean soup was thus: The phrase “2 bean soup” was entered into the search engine, and the result recorded. Next, the phase “two bean soup” was entered. The search term producing the largest number was recorded as the most accurate number. This method was repeated until the number of beans in soup failed to produce relevant returns, thus, “Page 34, beans are the flavor of the month for soup…” was not considered a valid return for ‘34 bean soup.’

The chart (see Figure 2) plots the number of pages returned for each number of varieties of bean in soups for bean quantities ranging from 2 to 23. No soups were found using in excess of 23 varieties of bean.

Figure 2. A graph of the data. This depicts the number of World Wide Web pages the author found that pertain to each number of varieties of bean in soups for bean quantities ranging from 2 to 23.

Taking the pulse of bean soup is less straight forward than originally supposed. I had reckoned to encounter a normal bell curve with a peak around 16 beans, as the diversity of recipes for bean soup would at first sight seem to be a random event.

However, the presence of six distinct peaks at 3, 5 , 9, 13, 15 and 19 beans is nothing less than startling. (One must note that these numbers do not constitute a Fibonacci sequence, despite their approximate similarity to one.). Four of the six peak numbers of beans are prime, and the remaining two numbers are the square of the first number and the product of the first two.

Multiple bean soup looks to be anything but a random phenomenon.

Figure 1. Three kinds of beans (in cans). Photo: S. Drew.

Pushing the Packet

Further investigation of these relationships failed to locate a 25-bean soup (25 is 5²) or 45 bean soup (45 is 5 x 9). Similarly, I have not been able to determine why prime numbers 7, 11 and 17 fail to make popular soup. 11- and 17-bean soups have turned in a particularly alarming performance, with only 21 and 12 references, respectively. (One must note the coincidental numeric palindrome formed by the digits 2 and 1.)

Not being a cook, I cannot explain why the three major peaks occur around the numbers 3, 5 and 15. The mathematical relationship between the three numbers is startling (3 x 5 =15). Food historians may know of rules of proportion governing cuisine wherein years of experimentation have yielded rules for the use of ingredients in fixed mathematical proportions.

The distance between successive peaks (major and minor: 3, 5, 9, 13, 15 19) occurs in a regular pattern as well: 2, 4, 4, 2, 4. (2, of course, is the second prime number, and 4 is its square). It is interesting to note that continuing the pattern (19 + 4) takes us to the prime number 23, which is the largest number of beans found in soup during our investigation.

Some Possible Explanations

There are many possible explanation for these and other patterns that are evident in this referential-bean counting exercise. Several have to do with oddity and evenness.

The visual appeal of objects odd or prime in number is well known, but given the nature of soup, the number of beans used in its preparation is not readily noticeable — either consciously or unconsciously — the soup eater, or even to someone who simply observes or handles the finished soup.

Seasonal influences are not applicable, because most recipes use dried, rather than fresh, beans. Moving a step back in the process, the sale of beans is apt to be related to the marketing of the beans, in that prime and odd numbers are displayed at wholesale and retail outlets.

Alternatively, there is the so-called Beethoven phenomenon. Beethoven’s odd numbered symphonies are universally acknowledged to be superior to his even numbered ones. The appeal of odd numbers may stem from the fact that humans are, for the most part symmetrical, so there may be a special appeal for things with odd numbers, which would be considered exotic.

Another possibility arises at the chemical level, where the interaction of odd or prime numbers of ingredients may produce more desirable flavors than do even or non-prime numbers. (A quick glance through a book of cocktail recipes suggests there is something to this, but it requires considerable further investigation.)

Summary

The socio-cultural origins of bean soup may be found to play an important part in unravelling the mathematical aspects of recipes in general and of multi-bean soup in particular. (A doctoral thesis titled ‘Multi-bean soups in multi-cultural societies’ is certainly in the offing.)

Conclusions

While not significantly nearer knowing why the composition of multi-bean soups tends to cluster around certain numbers of beans, it has been shown that the number of beans in soups is not random and seems to have a purposiveness. That the clusters are around prime numbers (and their multiples) may be a statistical aberration arising from the fact that there is a disproportionately large number of prime numbers between 1 and 23 (there are 10 of them). However, the peaks are pronounced enough around the three major peaks (3, 5, 15) suggest that something else has a major influence over bean soup recipes. Further research is clearly called for.

TABLE 1

The Raw Data: Collected using www.google.com on 1 February 2002
Number of Beans in Soup | Number of References Returned by Search Engine
2 109
3 261
4 35
5 336
6 108
7 100
8 29
9 128
10 118
11 21
12 49
13 98
14 26
15 355
16 166
17 12
18 44
19 128
20 3
21 3
23 1

(As a bonus, here is a recipe from Flickr user Bill McKee.)

_____________________

The article above is republished with permission from the March-April 2003 issue of the Annals of Improbable Research. You can download or purchase back issues of the magazine, or subscribe to receive future issues. Or get a subscription for someone as a gift!

Visit their website for more research that makes people LAUGH and then THINK.

By 2012, the number of robots worldwide could equal the number of people in the state of Illinois. The linked infograph has a list of statistics on how robots are taking over the workforce (on their way to taking over the world.)

Link -via Holy Kaw

However, it is the last graphic – the successor to the “bat’s wing” which I will call the “wedges” – that Nightingale is most famous for.  Strangely enough, the name that many people give it is wrong.  This graphic is not what Nightingale referred to as the “coxcomb”!

In this diagram, Nightingale resolved the problem of the “bat’s wing” by using areas to represent the variation in the death rate, instead of the length of radial lines.  The blue wedges, representing death by sickness, are far bigger than those representing wounds.  The message of this graphic is twofold: first, most of the fatalities during the war were from sickness and second, improvements in hygiene dramatically reduced the death rate.

Link -via Metafilter

See the full-size 1858 graph at Wikipedia.

Overall, “Transportation and material moving occupations”—people who work operating vehicles—dominated the death list, with 1,115 killed on the job. Only seven percent of them were murdered.

The 45-54 year-old bracket made up the plurality of deaths, with a full quarter. 16% of them plummeted to their demises.

The deadliest state to work in? Texas, with 456 fatalities. The safest? New Hampshire, with only 5. West Virginia won the explosion death contest, with 34—likely from all that coal mining, which is extremely dangerous and explosion-prone.

Happy Labor Day! Link -via the Presurfer

Etsy seller Nausicaa Distribution sells these precious statistical distribution graph plushies that are just perfect for any Neatorama reader. Of course, if you’re looking for cute nerdy plushes, the Neatoshop is also a great place to go shopping.

Link Via Laughing Squid

Herman Chernoff

A smiley-face is very expressive, statistically. By tweaking the eyes, mouth and other bits, you can literally put a meaningful face on any jumble of numbers. Herman Chernoff pointed this out in 1973 in the Journal of the American Statistical Association, in a monograph called “The Use of Faces to Represent Points in K-Dimensional Space Graphically.”

Subsequently, folks took to calling these things Chernoff faces. Chernoff faces can make statistical analysis into a recognizably human activity.

Most people, when shown some statistics, sigh and get boggled. But Herman Chernoff realized that almost everyone is good at reading faces. So he devised recipes to convert any set of statistics into an equivalent bunch of smiley-face drawings.

Each data point, he wrote, “is represented by a cartoon of a face whose features, such as length of nose and curvature of mouth, correspond to components of the point. Thus every multivariate observation is visualized as a computer-drawn face. This presentation makes it easy for the human mind to grasp many of the essential regularities and irregularities present in the data.”

“The Use of Faces to Represent Points in K-Dimensional Space Graphically” is one of the few statistics papers that is visually goofy, rather than arid.

One page is filled with 87 cartoon faces, each slightly different. Some faces have little beady eyes, others have big, startled-wideawake peepers. There are wide mouths, little dried-up “I’m not here, don’t notice me” mouths, and middling mouths. Another page shows off some of the cartoony variety that’s possible: roundish simpleton heads, jowly alien-visitor heads, and a smattering of noggins that look froggy. Elsewhere, the study perhaps inevitably includes conventional statistics machinery — charts of numbers, differential and intergral calculus equations, and plenty of technical lingo.

Chernoff discovered, by experiment, that people could comfortably interpret a face that expresses quite large amounts of data. “At this point,” he wrote, “one can treat up to 18 variables, but it would be relatively easy to increase that number by adding other features such as ears, hair, [and] facial lines.”

Chernoff faces made from data gathered by measuring rocks, and presented in Chernoff’s original paper in the Journal of the American Statistical Association. The paper explains that “Eight measurements were made on each of 88 nummulited specimens from the Eocene Yellow Limestone Formation of northwestern Jamaica.”

The world has gone on to employ Chernoff faces a little, but not yet a lot. A 1981 report in the Journal of Marketing, for example, used them to display corporate financial data, with this explanation: “From Year 5 to Year 1, the nose narrows as well as increases in length, and the eccentricity of the eyes increases. Respectively, these facial features represent a decrease in total assets, an increase in the ratio of retained earnings to total assets, and an  increase in cash flow.”

A note at the very end of Chernoff’s 1973 paper hints at a practical reason why his idea would not catch on immediately: “At this time the cost of drawing these faces is about 20 to 25 cents per face on the IBM 360-67 at Stanford University using the Calcomp Plotter. Most of this cost is in the computing, and I believe that it should be possible to reduce it considerably.”

Chernoff faces representing data about a series of Swiss bank notes, some real, some forged, from Bernhard Flury and Hans Riedwyl,’s 1981 study in the Journal of the American Statistical Association. The main variables are:
Xl length of the bank note
X2 width of the bank note, measured on the left side
X3 width of the bank note, measured on the right side
X4 width of the lower margin
X5 width of the upper margin
X6 length of the print diagonal from the lower left to the upper right corner

References
“The Use of Faces to Represent Points in K-Dimensional Space Graphically,” Herman Chernoff, Journal of the American Statistical Association, vol. 68, no. 342, 1973, pp. 361–8.

“Facial Representation of Multivariate Data,” David L. Huff, Vijay Mahajan and William C. Black, Journal of Marketing, vol. 45, no. 4, Autumn 1981, pp. 53-9.

“Use of Chernoff Faces to Follow Trends in Laboratory Data,” John A. Lott and Timothy C. Durbridge, Journal of Clinical Laboratory Analysis, 1990, pp. 459-63. The authors are at Ohio State University in the USA and the Institute of Medical and Veterinary Sciences in Adelaide, Australia.

“Graphical Representation of Multivariate Data by Means of Asymmetrical Faces,” Bernhard Flury and Hans Riedwyl, Journal of the American Statistical Association, vol. 76, no. 376, December 1981, pp. 757-65. The authors are at the University of Berne, Switzerland.

_____________________

This article is republished with permission from the July-August 2010 issue of the Annals of Improbable Research. You can download or purchase back issues of the magazine, or subscribe to receive future issues. Or get a subscription for someone as a gift!

Visit their website for more research that makes people LAUGH and then THINK.

Josh Sundquist shares some charts and graphs about fireworks, pie, and other Independence Day traditions. -via Buzzfeed

I fall under the group of “cat people,” though I admit that this isn’t exactly by choice. The fact that I have a cat but no dog indicates a little bit about my personality, according to a survey by hunch.com. For instance, I am more likely than dog people to skew liberal, live in a city, own an Android device, and be self-professedly fashion-challenged. (Two of these are correct; I’ll leave the guessing up to you.)

What does your pet-owner status say about you? Check out the infographic at Hunch and find out. Link

Every year extraordinary weather events rock the Earth. Records that have stood centuries are broken. Great floods, droughts, and storms affect millions of people, and truly exceptional weather events unprecedented in human history may occur. But the wild roller-coaster ride of incredible weather events during 2010, in my mind, makes that year the planet’s most extraordinary year for extreme weather since reliable global upper-air data began in the late 1940s. Never in my 30 years as a meteorologist have I witnessed a year like 2010–the astonishing number of weather disasters and unprecedented wild swings in Earth’s atmospheric circulation were like nothing I’ve seen. The pace of incredible extreme weather events in the U.S. over the past few months have kept me so busy that I’ve been unable to write-up a retrospective look at the weather events of 2010. But I’ve finally managed to finish, so fasten your seat belts for a tour through the top twenty most remarkable weather events of 2010.

What follows is a rundown of weather events from “Snowmageddon” to record flooding the world over, with plenty of graphs, charts, and videos to explain them. Link -via Fark

The exhibit “Of All The People in All The World” is traveling the world to show all the people exactly how many of them there are by using rice. Sometimes it’s hard to get your mind around millions and billions of humans and their varying statistics. By representing each person as a single piece of rice this showcase helps people understand varying numbers such as how many people have walked on the moon and the population of the United States (pictured). See link for more in depth video.

The show adapts to its setting: the country, city and building it is in. The amount of rice used varies according to which version is performed, Of All The People In All The World: UK is a standard small version using 1,000Kg of rice to represent 60,000,000 people. Of All The People In All The World: Europe at 12,000Kg is an example of a medium size version. So far there has been a single presentation of Of All The People In All The World in which the whole world’s population was represented by 104 tons of rice in Stuttgart.

Link

Statistically speaking, anyways. Every year, Gallup polled randomly selected American adults about their lives, including how happy they are:

The New York Times asked Gallup to come up with a statistical composite for the happiest person in America, based on the characteristics that most closely correlated with happiness in 2010. Men, for example, tend to be happier than women, older people are happier than middle-aged people, and so on.

Gallup’s answer: he’s a tall, Asian-American, observant Jew who is at least 65 and married, has children, lives in Hawaii, runs his own business and has a household income of more than $120,000 a year.

Does that person actually exist? The New York Times went a-sleuthin':

A few phone calls later and ... Meet Alvin Wong. He is a 5-foot-10, 69-year-old, Chinese-American, Kosher-observing Jew, who’s married with children and lives in Honolulu. He runs his own health care management business and earns more than $120,000 a year.

Link

Whether you prefer apples or oranges, this tongue-in-cheek infographic is one worth reading. I especially like this “pie chart” which is, of course, a bar chart. And you might see a little bias in the author’s stats. Link -via Buzzfeed

“… I start looking at the tic-tac-toe game, and I begin to wonder how they make these things,” Srivastava says. “The tickets are clearly mass-produced, which means there must be some computer program that lays down the numbers. Of course, it would be really nice if the computer could just spit out random digits. But that’s not possible, since the lottery corporation needs to control the number of winning tickets. The game can’t be truly random. Instead, it has to generate the illusion of randomness while actually being carefully determined.”

He discovered that the numbers on the card before scratching provided information about the numbers underneath the latex. Specifically, he found that “singletons” – numbers present only once on a card – were likely to indicate the location of a successful scratch. After cracking the code, he calculated that he could win about $600/day if he spent full-time buying and scratching cards.  Instead, he took his information to the Ontario Lottery and Gaming Corporation.

A sample card is shown at left.  Details of his logic and calculations are explained at the Wired link.

Link.

I’ve found myself watching a lot of Law & Order reruns lately, mostly because you can find it most hours of the day on various TV channels. Now we have statistics that track the outcomes of the cases in each season. Not only that, but they are compared with what was actually going on in New York City at the time -the NYC murder rate and the politics of municipal law enforcement. Some of the changes in the show over time reflect the real world, and other changes were made for the TV audience. Read the analysis at, appropriately enough, Overthinking It. Link -via Metafilter

Google has a new tool in which you can search the books that Google has digitized for a keyword and get statistics from as far back as the 16th century. The Google Book Ngram Viewer gives you data you can use to track the popularity of …just about anything. For example, Geeks Are Sexy looked as terms like “geek”, “computer geek”, and “computer hacker” to ascertain that geeks are indeed, gaining in popularity. Link

The New York Times has a series of interactive maps of the US with which you can study population distribution by race and ethnicity, income, housing (such as mortgage, home value, and rent), and by education. I found that the average household income in my county in 2009 was $21,195, which is 10% less than in the year 2000. The data comes from the US Census Bureau. Link -via Metafilter

The German tanks were numbered as follows: 1, 2, 3 … N, where N was the desired total number of tanks produced. Imagine that they had captured five tanks, with serial numbers 20, 31, 43, 78 and 92. They now had a sample of five, with a maximum serial number of 92. Call the sample size S and the maximum serial number M. After some experimentation with other series, the statisticians reckoned that a good estimator of the number of tanks would probably be provided by the simple equation (M-1)(S+1)/S. In the example given, this translates to (92-1)(5+1)/5, which is equal to 109.2. Therefore the estimate of tanks produced at that time would be 109

By using this formula, statisticians reportedly estimated that the Germans produced 246 tanks per month between June 1940 and September 1942. At that time, standard intelligence estimates had believed the number was far, far higher, at around 1,400. After the war, the allies captured German production records, showing that the true number of tanks produced in those three years was 245 per month, almost exactly what the statisticians had calculated, and less than one fifth of what standard intelligence had thought likely.

Link via Now I Know | Photo of Tiger II tank by Flickr user cliff1066 used under Creative Commons license

Surprise, surprise! The average American worker slacks off about 1.7 hours per work day. Almost half of them spent this time unproductively on the Internet (sorry, guys! I know Neatorama’s not helping your productivity!), 1/5th socialized instead of worked, and the rest … Oh, I’ll finish this post l8r.

Link

Before you chalk this one up as the discredited pseudoscience of physiognomy rearing its ugly head, consider the argument behind a paper by Georgia State University economist (and dashingly handsome guy) Erdal Tekin:

Ugly people are more likely to break the law. This is the statistically based conclusion in a paper published in The Review of Economics and Statistics entitled Ugly Criminals [...]

This takes us to the modern Ugly Criminals study, which is subtler than it might seem. It is based on an anonymous questionnaire combined with equally anonymous ratings of the subject’s attractiveness. It shows a small but significant correlation between attractiveness, or the lack of it, and criminality. The most unattractive segment are 1.5 per cent more likely to have committed robbery, 2.2 per cent more likely to have committed assault, and 3 per cent more likely to have sold drugs. Or to have been caught doing so, at any rate.

The authors note previous work showing how more attractive people are more successful in their careers and earn more. This puts less attractive people at a disadvantage in the world of work and nudges them towards criminal alternatives. In addition, less attract ive people suffer socially, make fewer friends and build less of what the authors call “human capital”. They are therefore not as sympathetic to others and have less of an investment in society. This effect is far more pronounced in females, suggesting that they are judged on their appearance to a much greater degree.

Links: Article at Fortean Times | The paper Ugly Criminals by H. Naci Mocan and Erdal Tekin [PDF]

This chart from the utility company EPCOR contrasts water consumption in Edmonton, Alberta on two different days. February 27th is charted to show a normal day. February 28th was the day Canada won the Olympic gold medal hockey game. As you can see, a huge percentage of their customers waited for breaks in the game to “go”. Link -via reddit

Take a look at exactly who is using the Internet the most, how they are using it and how much the amount of usage is increasing. We can see that there are the same number of men and women who use the Internet. However, their age, educational background and level of income may influence how much time they spend online. Pictured is a small part of the much larger infographic.

Link

From the Upcoming ueue, submitted by psogle .

Topping each of the sin categories were South Korea (lust), the US (gluttony), Mexico (greed), Iceland (sloth and pride), South Africa (wrath) and Australia (envy).

Of course, some question the research methods and the results. Link -via Simply Left Behind

The New York Times has an interactive graph that plots the jobless rate for different groups of Americans compared to the average for all those who are unemployed. Mouseover to find lines for different races, ages, and levels of education. In this screenshot, the label refers to the very faint red line above the dotted line which represents the average jobless rate. Link -via Metafilter

According to a survey of accident claims by Churchill Car Insurance, computers aren't the only thing computer programmers engineers like to crash - they are also likely to crash their cars ...

Here are the 10 "most likely to crash" occupations:

1. Computer engineer
2. Sales manager
3. Chef
4. Student
5. Doctor
6. Estate agent
7. Surveyor
8. Customer adviser
9. Hairdresser
10. Social worker

In contrast, farmers had the best road safety records based on claims made, followed by aircraft fitters, stores personnel and ambulance drivers.

Link

That reminded me of the classic programmer joke ... from Stack Overflow:

A physicist, an engineer and a programmer were in a car driving over a steep alpine pass when the brakes failed. The car was getting faster and faster, they were struggling to get round the corners and once or twice only the feeble crash barrier saved them from crashing down the side of the mountain. They were sure they were all going to die, when suddenly they spotted an escape lane. They pulled into the escape lane, and came safely to a halt.

The physicist said "We need to model the friction in the brake pads and the resultant temperature rise, see if we can work out why they failed".

The engineer said "I think I've got a few spanners in the back. I'll take a look and see if I can work out what's wrong".

The programmer said "Why don't we get going again and see if it's reproducible?"

An outbreak of zombies infecting humans is likely to be disastrous, unless extremely aggressive tactics are employed against the undead. While aggressive quarantine may eradicate the infection, this is unlikely to happen in practice. A cure would only result in some humans surviving the outbreak, although they will still coexist with zombies. Only sufficiently frequent attacks, with increasing force, will result in eradication, assuming the available resources can be mustered in time.

Well, that was fairly obvious. But now there’s hard science to back up common sense, and the academic community is starting to take the undead threat seriously.

Link via io9

Image by flickr user ingridjee used under creative commons license

Still, the University of Vermont study presents what could be a complementary measure, and it provides a few decent cocktail-party nuggets along the way. Dr. Dodds and Dr. Danforth downloaded the lyrics to 232,574 songs by 20,025 artists released between 1960 and 2007, from the Web site hotlyrics.net. From another site, wefeelfine.org, they pulled more than nine million sentences that used some form of the verb feel — as in “I feel relieved” — from 2.3 million blogs from 2005 to 2009. They also analyzed State of the Union speeches going back to George Washington’s. They then rated the psychological charge, or “valence,” of a significant subset of the words on a 10-point scale: from triumphant (8.82) and love (8.72) down to disgusted (2.45) and suicide (1.25).

Some of the findings were expected. Sept. 11, 2001, was rock bottom, for instance. Others were less so: the day that Michael Jackson died also lowered people’s mood significantly. The high-water mark was the day President Obama was elected, when the word “proud” was predominant.

Christmas and Valentine’s Day regularly popped as positive times, although words like “guilty” were associated with Christmas and “waste” and “lonely” with Valentine’s Day.

Link via Hit & Run

Dodds and Danforth’s Peer-Reviewed Article

This chart shows how heavy metal band names are related to each other and how you can place them in various categories – like deadly things and badass misspellings.

Link – via kottke.org