if you see hoof prints, think horses—not zebras

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pages: 509 words: 92,141

The Pragmatic Programmer by Andrew Hunt, Dave Thomas

A Pattern Language, Broken windows theory, business process, buy low sell high, c2.com, combinatorial explosion, continuous integration, database schema, domain-specific language, don't repeat yourself, Donald Knuth, general-purpose programming language, George Santayana, Grace Hopper, if you see hoof prints, think horses—not zebras, index card, lateral thinking, loose coupling, Menlo Park, MVC pattern, premature optimization, Ralph Waldo Emerson, revision control, Schrödinger's Cat, slashdot, sorting algorithm, speech recognition, traveling salesman, urban decay, Y2K

He spent weeks writing work-arounds, which, for some odd reason, didn't seem to fix the problem. When finally forced to sit down and read the documentation on select, he discovered the problem and corrected it in a matter of minutes. We now use the phrase "select is broken" as a gentle reminder whenever one of us starts blaming the system for a fault that is likely to be our own. Tip 26 "select" Isn't Broken Remember, if you see hoof prints, think horses—not zebras. The OS is probably not broken. And the database is probably just fine. If you "changed only one thing" and the system stopped working, that one thing was likely to be responsible, directly or indirectly, no matter how farfetched it seems. Sometimes the thing that changed is outside of your control: new versions of the OS, compiler, database, or other third-party software can wreak havoc with previously correct code.

The Autistic Brain: Thinking Across the Spectrum by Temple Grandin, Richard Panek

Asperger Syndrome, correlation does not imply causation, dark matter, David Brooks, deliberate practice, double helix, ghettoisation, if you see hoof prints, think horses—not zebras, impulse control, Khan Academy, Mark Zuckerberg, meta analysis, meta-analysis, mouse model, neurotypical, pattern recognition, phenotype, Richard Feynman, selective serotonin reuptake inhibitor (SSRI), Silicon Valley, Steve Jobs, theory of mind, twin studies

Oral ARBs—bar and fence chewing, obsessive object licking, tongue rolling, and so on. Oral stereotypies are common in all grazing animals, because that’s what they do all day. They graze. Other ARBs—rocking, repetitive jumping, and so on, or “non-locomotory body movements.” The zoo animals I call the “big pretty animals”—the big predators such as the lions, tigers, and bears—pace. Ungulates, which are the hoofed animals—horses, cows, rhinoceroses, pigs, zebras, llamas—do stereotypies with their mouths. Most of the other animals, including primates and lab rats, develop movement stereotypies in the third category. In human disorders such as autism, the abnormal behavior is usually in the first or third category. One of the most extreme cases of stereotypy I’ve ever seen was in a female wolf I saw at a wolf shelter. The wolf’s name was Luna.

pages: 364 words: 112,681

Moneyland: Why Thieves and Crooks Now Rule the World and How to Take It Back by Oliver Bullough

banking crisis, Bernie Madoff, bitcoin, blood diamonds, Bretton Woods, BRICs, British Empire, capital controls, central bank independence, corporate governance, cryptocurrency, cuban missile crisis, dark matter, diversification, Donald Trump, energy security, failed state, Flash crash, Francis Fukuyama: the end of history, full employment, high net worth, if you see hoof prints, think horses—not zebras, income inequality, joint-stock company, liberal capitalism, liberal world order, mass immigration, medical malpractice, offshore financial centre, plutocrats, Plutocrats, Plutonomy: Buying Luxury, Explaining Global Imbalances, rent-seeking, Richard Feynman, risk tolerance, Sloane Ranger, sovereign wealth fund, WikiLeaks

Why, if he lived permanently in Muswell Hill, did he tell the ambulance crew that his family doctor was ‘in Russia’, for example? And why, as Cole had asked him when that first ambulance attended his house, if he had such a quintessentially English name, did he sound so very Russian? But these were tiny details only picked up later. Any doctor will tell you that the guiding mantra for dealing with newly arrived patients is that common things are common. If you see hoof prints, think horses, not zebras; if you see an otherwise healthy man vomiting and suffering from diarrhoea, think gastroenteritis, not assassination ordered at the highest levels of a foreign government. The next morning, Carter was started on ciprofloxacin, an antibiotic that would combat any nasty bugs that were upsetting his stomach. That was what is called an empirical decision. The doctors did not yet know what was causing the problem, but they were making an informed guess that he had some kind of food poisoning, and were treating him accordingly.

pages: 492 words: 149,259

Big Bang by Simon Singh

Albert Einstein, Albert Michelson, All science is either physics or stamp collecting, Andrew Wiles, anthropic principle, Arthur Eddington, Astronomia nova, Brownian motion, carbon-based life, Cepheid variable, Chance favours the prepared mind, Commentariolus, Copley Medal, cosmic abundance, cosmic microwave background, cosmological constant, cosmological principle, dark matter, Dava Sobel, Defenestration of Prague, discovery of penicillin, Dmitri Mendeleev, Edmond Halley, Edward Charles Pickering, Eratosthenes, Ernest Rutherford, Erwin Freundlich, Fellow of the Royal Society, fudge factor, Hans Lippershey, Harlow Shapley and Heber Curtis, Harvard Computers: women astronomers, Henri Poincaré, horn antenna, if you see hoof prints, think horses—not zebras, Index librorum prohibitorum, invention of the telescope, Isaac Newton, Johannes Kepler, John von Neumann, Karl Jansky, Kickstarter, Louis Daguerre, Louis Pasteur, luminiferous ether, Magellanic Cloud, Murray Gell-Mann, music of the spheres, Olbers’ paradox, On the Revolutions of the Heavenly Spheres, Paul Erdős, retrograde motion, Richard Feynman, scientific mainstream, Simon Singh, Solar eclipse in 1919, Stephen Hawking, the scientific method, Thomas Kuhn: the structure of scientific revolutions, unbiased observer, Wilhelm Olbers, William of Occam

A more complicated hypothesis might be that two meteorites simultaneously arrived from outer space, each ricocheting off one tree, felling the trees in the process, and then the meteorites collided head on with each other and vaporised, thereby accounting for the lack of any material evidence. Applying Occam’s razor, you decide that the storm, rather than the twin meteorites, is the more likely explanation because it is the simpler one. Occam’s razor does not guarantee the right answer, but it does usually point us towards the correct one. Doctors often rely on Occam’s razor when diagnosing an illness, and medical students are advised: ‘When you hear hoof beats, think horses, not zebras.’ On the other hand, conspiracy theorists despise Occam’s razor, often rejecting a simple explanation in favour of a more convoluted and intriguing line of reasoning. Occam’s razor favoured the Copernican model (one circle per planet) over the Ptolemaic model (one epicycle, deferent, equant and eccentric per planet), but Occam’s razor is only decisive if two theories are equally successful, and in the sixteenth century the Ptolemaic model was clearly stronger in several ways; most notably, it made more accurate predictions of planetary positions.

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pages: 1,758 words: 342,766

Code Complete (Developer Best Practices) by Steve McConnell

Ada Lovelace, Albert Einstein, Buckminster Fuller, call centre, continuous integration, data acquisition, database schema, don't repeat yourself, Donald Knuth, fault tolerance, Grace Hopper, haute cuisine, if you see hoof prints, think horses—not zebras, index card, inventory management, iterative process, Larry Wall, loose coupling, Menlo Park, Perl 6, place-making, premature optimization, revision control, Sapir-Whorf hypothesis, slashdot, sorting algorithm, statistical model, Tacoma Narrows Bridge, the scientific method, Thomas Kuhn: the structure of scientific revolutions, Turing machine, web application

A pair of studies performed many years ago found that, of total errors reported, roughly 95% are caused by programmers, 2% by systems software (the compiler and the operating system), 2% by some other software, and 1% by the hardware (Brown and Sampson 1973, Ostrand and Weyuker 1984). Systems software and development tools are used by many more people today than they were in the 1970s and 1980s, and so my best guess is that, today, an even higher percentage of errors are the programmers' fault. If you see hoof prints, think horses—not zebras. The OS is probably not broken. And the database is probably just fine. — Andy Hunt Dave Thomas Clerical errors (typos) are a surprisingly common source of problems. One study found that 36% of all construction errors were clerical mistakes (Weiss 1975). A 1987 study of almost 3 million lines of flight-dynamics software found that 18% of all errors were clerical (Card 1987).

The Greatest Show on Earth: The Evidence for Evolution by Richard Dawkins

Alfred Russel Wallace, Andrew Wiles, Arthur Eddington, back-to-the-land, Claude Shannon: information theory, correlation does not imply causation, Craig Reynolds: boids flock, Danny Hillis, David Attenborough, discovery of DNA, Dmitri Mendeleev, double helix, en.wikipedia.org, epigenetics, experimental subject, if you see hoof prints, think horses—not zebras, invisible hand, Louis Pasteur, out of africa, phenotype, Thomas Malthus

But it would be a good guess (supported by fossil evidence, as it happens, but we aren’t talking about fossils in this chapter) that the shared ancestor probably looked more like the okapi than the giraffe. Similarly, impalas and gnus* are close cousins of each other, and slightly more distant cousins of giraffes and okapis. All four of them are more distant cousins still of other cloven-hoofed animals, such as pigs and warthogs (which are cousins of each other and of peccaries). All the cloven-hoofed animals are more distant cousins of horses and zebras (which don’t have cloven hooves and are close cousins of each other). We can go on as long as we like, bracketing pairs of cousins into groups, and groups of groups of cousins, and (groups of (groups of (groups of cousins))). I have slipped into using brackets automatically, and you know just what they signify. The meaning of the brackets in the following is immediately clear to you, because you already know all about cousins sharing grandparents, and second cousins sharing great-grandparents, and so on: {(wolf fox)(lion leopard)}{(giraffe okapi) (impala gnu)} Everything points to a simple branching tree of ancestry – a family tree.

pages: 561 words: 167,631