It’s science’s dirtiest secret: The “scientific method” of testing hypotheses by statistical analysis stands on a flimsy foundation. Statistical tests are supposed to guide scientists in judging whether an experimental result reflects some real effect or is merely a random fluke, but the standard methods mix mutually inconsistent philosophies and offer no meaningful basis for making such decisions. Even when performed correctly, statistical tests are widely misunderstood and frequently misinterpreted. As a result, countless conclusions in the scientific literature are erroneous, and tests of medical dangers or treatments are often contradictory and confusing.

You may have noticed another characteristic of contrarian climate research – there is no cohesive, consistent alternative theory to human-caused global warming. Some blame global warming on the sun, others on orbital cycles of other planets, others on ocean cycles, and so on. There is a 97% expert consensus on a cohesive theory that’s overwhelmingly supported by the scientific evidence, but the 2–3% of papers that reject that consensus are all over the map, even contradicting each other. The one thing they seem to have in common is methodological flaws like cherry picking, curve fitting, ignoring inconvenient data, and disregarding known physics.

Interestingly, both groups are using the same data, and both groups claim that the other is misrepresenting the data for their own purposes. As I will demonstrate, however, it is the anti-vaccers which are ignoring the rules of statistical analysis and manipulating the data to tell an inaccurate story.

To run a test that asks an important question, that uses a large enough sample size to come to a reliable conclusion, and that can do so amidst a minefield of different ways to be lead astray, takes a lot of resources.

You have to design the test, implement the technology, and come up with the various options. If you’re running a lean organization, there are few cases where this is worth the effort.

Why create a half-assed “A” and a half-assed “B,” when you could just make a full-assed “A?”

In this article we'll explore a neat way of visualizing your MP3 music collection. The end result will be a hexagonal map of all your songs, with similar sounding tracks located next to each other. The color of different regions corresponds to different genres of music (e.g. classical, hip hop, hard rock). As an example, here's a map of three albums from my music collection: Paganini's Violin Caprices, Eminem's The Eminem Show, and Coldplay's X&Y.

BayesDB, a Bayesian database table, lets users query the probable implications of their data as easily as a SQL database lets them query the data itself. Using the built-in Bayesian Query Language (BQL), users with no statistics training can solve basic data science problems, such as detecting predictive relationships between variables, inferring missing values, simulating probable observations, and identifying statistically similar database entries.

, which Wald saw instantly, was that the holes showed where the planes were strongest. The holes showed where a bomber could be shot and still survive the flight home, Wald explained. After all, here they were, holes and all. It was the planes that weren’t there that needed extra protection, and they had needed it in places that these planes had not. The holes in the surviving planes actually revealed the locations that needed the least additional armor. Look at where the survivors are unharmed, he said, and that’s where these bombers are most vulnerable; that’s where the planes that didn’t make it back were hit.

"The commonly held belief that entrepreneurs are young college students working out of their dorms is simply wrong," says study author Vivek Wadhwa of Duke University's Center for Entrepreneurship and Research Commercialization. "People typically come to a stage where they're tired of working for other people. They think, 'I'm 40 and I haven't made it big yet. This is my last chance.' That really spurs the entrepreneurial spirit."

People often think that the big city is a dangerous place: they worry that they might get murdered, for instance. Being killed on purpose is more likely in town, according to new research, but it is so rare compared to dying in an accident of some type that in fact you would be much more likely to die unexpectedly in the countryside - in America, anyway.