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Gabby: A Story of...
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Vladimir Nabokov
“Of the not very many ways known of shedding one's body, falling, falling, falling is the supreme method, but you have to select your sill or ledge very carefully so as not to hurt yourself or others. Jumping from a high bridge is not recommended even if you cannot swim, for wind and water abound in weird contingencies, and tragedy ought not to culminate in a record dive or a policeman's promotion. If you rent a cell in the luminous waffle, room 1915 or 1959, in a tall business centre hotel browing the star dust, and pull up the window, and gently - not fall, not jump - but roll out as you should for air comfort, there is always the chance of knocking clean through into your own hell a pacific noctambulator walking his dog; in this respect a back room might be safer, especially if giving on the roof of an old tenacious normal house far below where a cat may be trusted to flash out of the way. Another popular take-off is a mountaintop with a sheer drop of say 500 meters but you must find it, because you will be surprised how easy it is to miscalculate your deflection offset, and have some hidden projection, some fool of a crag, rush forth to catch you, causing you to bounce off it into the brush, thwarted, mangled and unnecessarily alive. The ideal drop is from an aircraft, your muscles relaxed, your pilot puzzled, your packed parachute shuffled off, cast off, shrugged off - farewell, shootka (little chute)! Down you go, but all the while you feel suspended and buoyed as you somersault in slow motion like a somnolent tumbler pigeon, and sprawl supine on the eiderdown of the air, or lazily turn to embrace your pillow, enjoying every last instant of soft, deep, death-padded life, with the earth's green seesaw now above, now below, and the voluptuous crucifixion, as you stretch yourself in the growing rush, in the nearing swish, and then your loved body's obliteration in the Lap of the Lord.”
― Vladimir Nabokov, Pale Fire

“In 1963, the chaos theorist Edward Lorenz presented an often-referenced lecture entitled “Does the Flap of a Butterfly’s Wings in Brazil Set Off a Tornado in Texas?” Lorenz’s main point was that chaotic mathematical functions are very sensitive to initial conditions. Slight differences in initial conditions can lead to dramatically different results after many iterations. Lorenz believed that this sensitivity to slight differences in the beginning made it impossible to determine an answer to his question. Underlying Lorenz’s lecture was the assumption of determinism, that each initial condition can theoretically be traced as a cause of a final effect. This idea, called the “Butterfly Effect,” has been taken by the popularizers of chaos theory as a deep and wise truth. However, there is no scientific proof that such a cause and effect exists. There are no well-established mathematical models of reality that suggest such an effect. It is a statement of faith. It has as much scientific validity as statements about demons or God.”
― David Salsburg, The Lady Tasting Tea: How Statistics Revolutionized Science in the Twentieth Century

Matthew Kelly
“Our present culture idolizes three practical philosophies that are eating away at the very fabric of our workforce and culture, our relationships, and our lives. The first of these practical philosophies is individualism. When most people today are faced with a decision, the question that seems to dominate their inner dialogue is, “What’s in it for me?” This question is the creed of individualism, which is based on an all-consuming concern for self. In the present climate, the most dominant trend governing the decision-making process is individualism. Have you ever tried to work with a team where all its members were rugged individualists? Have you ever tried to manage an individualist? No community, whether it is as small as a team or as large as a nation, can grow strong with this attitude. Individualism always weakens the community and causes the whole to suffer. In every instance it is a cancerous growth. The fruits of individualism are no secret to any of us: greed, selfishness, and exploitation.”
― Matthew Kelly, Off Balance: Getting Beyond the Work-Life Balance Myth to Personal and Professional Satisfact ion

“In high school algebra, someone had already worked out the formulas. The teacher knew them or could find them in the teacher’s manual for the textbook. Imagine a word problem where nobody knows how to turn it into a formula, where some of the information is redundant and should not be used, where crucial information is often missing, and where there is no similar example worked out earlier in the textbook. This is what happens when one tries to apply statistical models to real-life problems.”
― David Salsburg, The Lady Tasting Tea: How Statistics Revolutionized Science in the Twentieth Century

“Notice that the p value works by assuming there is no difference between your experimental groups. This is a counterintuitive feature of significance testing: if you want to prove that your drug works, you do so by showing the data is inconsistent with the drug not working. Because of this, p values can be extended to any situation where you can mathematically express a hypothesis you want to knock down. But p values have their limitations. Remember, p is a measure of surprise, with a smaller value suggesting that you should be more surprised. It’s not a measure of the size of the effect. You can get a tiny p value by measuring a huge effect — “This medicine makes people live four times longer” — or by measuring a tiny effect with great certainty. And because any medication or intervention usually has some real effect, you can always get a statistically significant result by collecting so much data that you detect extremely tiny but relatively unimportant differences. As Bruce Thompson wrote, Statistical significance testing can involve a tautological logic in which tired researchers, having collected data on hundreds of subjects, then conduct a statistical test to evaluate whether there were a lot of subjects, which the researchers already know, because they collected the data and know they are tired. This tautology has created considerable damage as regards the cumulation of knowledge.1”
― Alex Reinhart, Statistics Done Wrong: The Woefully Complete Guide

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