Nice Statistical Quotes

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And see those clouds?' 'Hard to miss' 'Those are cumulus clouds. Did you know that?' 'I'm sure I should.' They're the best ones.' 'How come?' Because they look the way clouds are supposed to look, the way you draw them when you're a kid. Which is nice, you know? ...
Jennifer E. Smith (The Statistical Probability of Love at First Sight)
Because we tend to be nice to other people when they please us and nasty when they do not, we are statistically punished for being nice and rewarded for being nasty.
Daniel Kahneman (Thinking, Fast and Slow)
Those are cumulus clouds. Did you know that?" "I'm sure I should." "They're the best ones." "How come?" "Because they look the way clouds are supposed to look, the way you draw them when you're a kid. Which is nice, you know? I mean, the sun never looks the way you drew it.
Jennifer E. Smith (The Statistical Probability of Love at First Sight)
There is nothing virtuous or noble about being "tolerant" of people whose attitudes and behaviors you approve of. If you don't defend the freedom of even those individuals whose attitudes and behaviors you find disgusting, narrow-minded and offensive, then you are not tolerant. To "tolerate" doesn't mean you like it or approve of it; it means only that you ALLOW it to EXIST--i.e., you refrain from violently interfering. The people who look to "government" to FORCE people to be "nice" are not tolerant.
Larken Rose
I'm not sure I even believe in marriage," Hadley says and he looks surprised. "Aren't you on your way to a wedding?" "Yeah," she says with a nod. "But that's what I mean." He looks at her blankly. "It shouldn't be this big fuss, where you drag everyone halfway across the world to witness your love. If you want to share your life together, fine. But it's between two people, and that should be enough. Why the big show? Why rub it in everyone's faces?" Oliver runs a hand along his jaw, obviously not quite sure what to think. "It sounds like its weddings you don't believe in," he says finally. "Not marriage." "I'm not such a big fan of either at the moment." "I don't know," he says. "I think they're kind of nice." "They're not," she insists. "They're all for show. You shouldn't need to prove anything if you really mean it. It should be a whole lot simpler than that. It should mean something." "I think it does," Oliver says quietly. "It's a promise." "I guess so," she says, unable to keep the sigh out of her voice. "But not everyone keeps that promise." she looks over toward the woman, still fast asleep. "Not everyone makes it fifty-two years, and if you do, it doesn't matter that you once stood in front of all those people and said that you would. The important part is that you had someone to stick by you all that time. Even when everything sucked.
Jennifer E. Smith (The Statistical Probability of Love at First Sight)
Statistically, the odds that any given rape was committed by a serial offender are around 90 percent," Lisak said. "The research is clear on this. The foremost issue for police and prosecutors should be that you have a predator out there. By reporting this rape, the victim is giving you an opportunity to put this guy away. If you decline to pursue the case because the victim was drunk, or had a history of promiscuity, or whatever, the offender is almost certainly going to keep raping other women. We need cops and prosecutors who get it that 'nice guys' like Frank are serious criminals.
Jon Krakauer (Missoula: Rape and the Justice System in a College Town)
As Tim Minchin put it in his song “If I Didn’t Have You”: Your love is one in a million; You couldn’t buy it at any price. But of the 9.999 hundred thousand other loves, Statistically, some of them would be equally nice.
Randall Munroe (What If?: Serious Scientific Answers to Absurd Hypothetical Questions)
Your love is one in a million; You couldn’t buy it at any price. But of the 9.999 hundred thousand other loves, Statistically, some of them would be equally nice.
Randall Munroe (What If?: Serious Scientific Answers to Absurd Hypothetical Questions)
we are statistically punished for being nice
Daniel Kahneman (Thinking, Fast and Slow)
Blake smiling at her through the mirror as he shaved. “I do believe I made you late to work, Mrs. Sanders,” he said, which didn’t have as nice a ring to it as Dr. Sanders, but maybe that was okay. Maybe it was enough to be Mrs. Sanders, maybe it was enough to have her Introduction to Statistics class, and her house, and her family. That dark girl. She saw her again, tried to shake her out of her mind. She’d been arrogant, that was her problem. So focused on what was next that she didn’t appreciate what she’d already gotten away with. She couldn’t let herself slip up like that again. She’d have to focus. Stay alert.
Brit Bennett (The Vanishing Half)
None of us was normal. But “normal,” as I’d learned in math, was just a statistical concept, an averaged smoothing out of all diverse and interesting permutations to some hypothetical midpoint so generalized it was unlikely to surprise or offend. Or to delight. Normal was nice. Normal was bland. Normal was damned boring. Our differences, our own brand of crazy, were what made each of us special and unique and fascinating.
Joanne Macgregor (The Law of Tall Girls)
Girls mature faster than boys, cost more to raise, and statistics show that the old saw about girls not knowing about money and figures is a myth. Girls start to outspend boys before puberty—and they manage to maintain this lead until death or an ugly credit manager, whichever comes first. Males are born with a closed fist. Girls are born with the left hand cramped in a position the size of an American Express card. Whenever a girl sees a sign reading, “Sale, Going Out of Business, Liquidation,” saliva begins to form in her mouth, the palms of her hands perspire and the pituitary gland says, “Go, Mama.” In the male, it is quite a different story. He has a gland that follows a muscle from the right arm down to the base of his billfold pocket. It's called “cheap.” Girls can slam a door louder, beg longer, turn tears on and off like a faucet, and invented the term, “You don't trust me.” So much for “sugar and spice and everything nice” and “snips and snails and puppydog tails.
Erma Bombeck (Motherhood: The Second Oldest Profession)
I had stumbled onto a significant fact of the human condition: the feedback to which life exposes us is perverse. Because we tend to be nice to other people when they please us and nasty when they do not, we are statistically punished for being nice and rewarded for being nasty.
Daniel Kahneman (Thinking, Fast and Slow)
Most people believed, correctly, that most normal North Africans tended to be relatively poor and therefore unlikely to be able to afford a new car, and on the basis of that statistical association their presumption was that the individual North African driver of a nice car was a criminal. Now they assume he is an Uber driver, which is clear progress.
Abhijit V. Banerjee (Good Economics for Hard Times: Better Answers to Our Biggest Problems)
The captain drummed his fingers on the console. He was afraid that he might soon be conducting his own research project to find out what happened to a statistically small sample of whaler captains who came back without a factory ship full of research material. He wondered what they did to you. Maybe they locked you in a room with a harpoon gun and expected you to do the honorable thing.
Terry Pratchett (Good Omens: The Nice and Accurate Prophecies of Agnes Nutter, Witch)
And never fall into that statistical macho trap that’s so prevalent in fly-fishing these days. If you keep score, you can be beaten, but if you refuse to compete you can leave the impression that you have long since risen above that kind of crap. When someone says to you, “I caught forty-eight trout and ten of them were twenty inches or better. How’d you do?” say, “Yeah, we got some. Couple nice ones, too.
John Gierach (Death, Taxes, and Leaky Waders: A John Gierach Fly-Fishing Treasury (John Gierach's Fly-fishing Library))
To teach students any psychology they did not know before, you must surprise them. But which surprise will do? Nisbett and Borgida found that when they presented their students with a surprising statistical fact, the students managed to learn nothing at all. But when the students were surprised by individual cases—two nice people who had not helped—they immediately made the generalization and inferred that helping is more difficult than they had thought.
Daniel Kahneman (Thinking, Fast and Slow)
Statistically, maybe. And linguistically. With a little sociology thrown in. Plus a deep and innate understanding of human nature. Think about the number two hundred. Sounds like a nice round figure, but it isn’t, really. No one says two hundred purely at random. People say a hundred, or a thousand. Or hundreds or thousands. Two hundred deaths sounds specific to me. Like a true number. Maybe rounded up from the high 180s or 190s, but it sounds to me like there’s information behind it. Enough to keep me interested, anyway. For instance. Speaking as an investigator.” Westwood said nothing.
Lee Child (Make Me (Jack Reacher, #20))
Each month Cohn brought Trump the latest Job Openings and Labor Turnover Survey, called JOLTS, conducted by the Bureau of Labor Statistics. He realized he was being an asshole by rubbing it in because each month was basically the same, but he didn’t care. “Mr. President, can I show this to you?” Cohn fanned out the pages of data in front of the president. “See, the biggest leavers of jobs—people leaving voluntarily—was from manufacturing.” “I don’t get it,” Trump said. Cohn tried to explain: “I can sit in a nice office with air conditioning and a desk, or stand on my feet eight hours a day. Which one would you do for the same pay?
Bob Woodward (Fear: Trump in the White House)
The Kappamaki, a whaling research ship, was currently researching the question: How many whales can you catch in one week? Except that, today, there weren’t any whales. The crew stared at the screens, which by the application of ingenious technology could spot anything larger than a sardine and calculate its net value on the international oil market, and found them blank. The occasional fish that did show up was barreling through the water as if in a great hurry to get elsewhere. The captain drummed his fingers on the console. He was afraid that he might soon be conducting his own research project to find out what happened to a statistically small sample of whaler captains who came back without a factory ship full of research material. He wondered what they did to you. Maybe they locked you in a room with a harpoon gun and expected you to do the honorable thing. This was unreal. There ought to be something.
Terry Pratchett (Good Omens: The Nice and Accurate Prophecies of Agnes Nutter, Witch)
Cohn assembled every piece of economic data available to show that American workers did not aspire to work in assembly factories. Each month Cohn brought Trump the latest Job Openings and Labor Turnover Survey, called JOLTS, conducted y the Bureau of Labor Statistics. He realized he was being an asshole by rubbing it in because each month was basically the same, but he didn't care. "Mr. President, can I show this to you?" Cohn fanned out the pages of data in front of the president. "See, the biggest leavers of jobs--people leaving voluntarily--was from manufacturing." "I don't get it," Trump said. Cohn tried to explain: "I can sit in a nice office with air conditioning and a desk, or stand on my feet eight hours a day. Which one would you do for the same pay?" Cohn added, "People don't want to stand in front of a 2,000 degree blast furnace. People don't want to go into coal mines and get black lung. For the same dollars or equal ollars, they're going to choose something else." Trump wasn't buying it. Severl times Cohn just asked the president, "Why do you have these views?" "I just do," Trump replied. "I've had these views for 30 years." "That doesn't mean they're right," Cohn said. "I had the view for 15 years I could play professional football. It doesn't mean I was right.
Bob Woodward (Fear: Trump in the White House)
How I Got That Name Marilyn Chin an essay on assimilation I am Marilyn Mei Ling Chin Oh, how I love the resoluteness of that first person singular followed by that stalwart indicative of “be," without the uncertain i-n-g of “becoming.” Of course, the name had been changed somewhere between Angel Island and the sea, when my father the paperson in the late 1950s obsessed with a bombshell blond transliterated “Mei Ling” to “Marilyn.” And nobody dared question his initial impulse—for we all know lust drove men to greatness, not goodness, not decency. And there I was, a wayward pink baby, named after some tragic white woman swollen with gin and Nembutal. My mother couldn’t pronounce the “r.” She dubbed me “Numba one female offshoot” for brevity: henceforth, she will live and die in sublime ignorance, flanked by loving children and the “kitchen deity.” While my father dithers, a tomcat in Hong Kong trash— a gambler, a petty thug, who bought a chain of chopsuey joints in Piss River, Oregon, with bootlegged Gucci cash. Nobody dared question his integrity given his nice, devout daughters and his bright, industrious sons as if filial piety were the standard by which all earthly men are measured. * Oh, how trustworthy our daughters, how thrifty our sons! How we’ve managed to fool the experts in education, statistic and demography— We’re not very creative but not adverse to rote-learning. Indeed, they can use us. But the “Model Minority” is a tease. We know you are watching now, so we refuse to give you any! Oh, bamboo shoots, bamboo shoots! The further west we go, we’ll hit east; the deeper down we dig, we’ll find China. History has turned its stomach on a black polluted beach— where life doesn’t hinge on that red, red wheelbarrow, but whether or not our new lover in the final episode of “Santa Barbara” will lean over a scented candle and call us a “bitch.” Oh God, where have we gone wrong? We have no inner resources! * Then, one redolent spring morning the Great Patriarch Chin peered down from his kiosk in heaven and saw that his descendants were ugly. One had a squarish head and a nose without a bridge Another’s profile—long and knobbed as a gourd. A third, the sad, brutish one may never, never marry. And I, his least favorite— “not quite boiled, not quite cooked," a plump pomfret simmering in my juices— too listless to fight for my people’s destiny. “To kill without resistance is not slaughter” says the proverb. So, I wait for imminent death. The fact that this death is also metaphorical is testament to my lethargy. * So here lies Marilyn Mei Ling Chin, married once, twice to so-and-so, a Lee and a Wong, granddaughter of Jack “the patriarch” and the brooding Suilin Fong, daughter of the virtuous Yuet Kuen Wong and G.G. Chin the infamous, sister of a dozen, cousin of a million, survived by everbody and forgotten by all. She was neither black nor white, neither cherished nor vanquished, just another squatter in her own bamboo grove minding her poetry— when one day heaven was unmerciful, and a chasm opened where she stood. Like the jowls of a mighty white whale, or the jaws of a metaphysical Godzilla, it swallowed her whole. She did not flinch nor writhe, nor fret about the afterlife, but stayed! Solid as wood, happily a little gnawed, tattered, mesmerized by all that was lavished upon her and all that was taken away!
Marilyn Chin
You know those statistics people are always spouting off, about teenage boys thinking about sex every seven seconds? Is that really true?” “Nope. And I just want to point out that you’re the one who keeps bringing up sex. I think teenage girls might be more obsessed than boys.” “Maybe,” I say, and his eyes widen, all excited. Hastily I add, “I mean, I’m definitely curious about it. It’s definitely a thought. But I don’t see myself doing it anytime soon. With anybody. Including you.” I can tell Peter is embarrassed, the way he rushes to say, “Okay, okay, I got it. Let’s just change the subject.” Under his breath he mutters, “I didn’t even want to talk about it in the first place.” It’s sweet that he’s embarrassed. I didn’t think he would be, with all his experience. I tug on his sweater sleeve. “At some point, when I’m ready, if I’m ready, I’ll let you know.” And then I pull him toward me and press my lips against his softly. His mouth opens, and so does mine, and I think, I could kiss this boy for hours. Mid-kiss, he says, “Wait, so we’re never having sex? Like ever?” “I didn’t say never. But not now. I mean, not until I’m really, really sure. Okay?” He lets out a laugh. “Sure. You’re the one driving this bus. You have been from the start. I’m still catching up.” He snuggles closer and sniffs my hair. “What’s this new shampoo you’re wearing?” “I stole it from Margot. It’s juicy pear. Nice, right?” “It’s all right, I guess. But can you go back to the one you used to wear? The coconut one? I love the smell of that one.” A dreamy look crosses his face, like evening fog settling over a city. “If I feel like it,” I say, which makes him pout. I’m already thinking I should buy a bottle of the coconut hair mask, too, but I like to keep him on his toes. Like he said, “I’m the one driving this bus. Peter pulls me against him so he’s curved around my back like shelter. I let my head rest on his shoulder, rest my arms on his kneecaps. This is nice. This is cozy. Just me and him, just for a while, apart from the rest of the world.
Jenny Han (P.S. I Still Love You (To All the Boys I've Loved Before, #2))
Christians often fail to get in touch with the shocking message that can lie at the heart of evangelism: “I am here to change you, and I’m going to change you so that you become like me.” There are some obvious dangers here once we think about all this. If we approach people in this way, we are not treating them as people. We are not respecting them. We are treating them as part of our own program, like an objective and a statistic, and this is self-centered as well as disrespectful. An obnoxious smell of superiority is apparent. Further, we are judging people as fundamentally inadequate. *We* are okay, of course. Missionary work conducted in this spirit is a well-intentioned but self-centered power-play… We can avoid this instrumentalizing of potential converts - a making of them into something like an instrument or tool that then does something for us - only by approaching them for their own sakes and hence not as potential converts at all. We must value our initial relationships with people for what they are and not in terms of what we want out of them. This means that we must want to become their friends. Moreover, it must be a friendship with no strings attached. We must seek out relationships because we are interested in and value other people for who they are, right where they are. Conversions would be nice, but they are not our main agenda. We hope and pray for the best for our new friends, but that is not our principal motivation for relating to them. In this way and only in this way do we avoid colonizing people as we convert them.
Douglas A. Campbell (Paul: An Apostle's Journey)
[I]t would be a niceness that was enforced leniently, patiently and gracefully, with the sort of unflappable self-certainty [they] couldn't help displaying when all its statistics proved that it really was doing the right thing.
Iain M. Banks
And while seeking out the opinions and perspectives of people like ourselves may lead to a more personal and familiar buying experience, what’s even more amazing is the impact those trusted sources have on conversion rates. B2B sales cycle data from Salesforce demonstrates that, when it comes to lead conversion, the interest that originates from customer and employee referrals converts to deals at rates fifty times higher than email campaigns!9 Furthermore, data from marketing automation giant Marketo indicates that leads originating from referrals convert to opportunities at rates of four times the average, and similar to the next three highest-converting lead sources combined (those being partner, inbound, and marketing-generated).10 My personal experience over the years greatly corroborates these statistics. For example, when I started my own sales practice, Cerebral Selling, I needed to have a logo designed. Around the same time, my friend had recently had a nice logo designed for his business. I asked him who he used, he told me, and I just did the same. No further research or investigation required. A short time later, I wanted to head out of town with my wife for an overnight trip to the beautiful Niagara wine region of Ontario to celebrate our anniversary. I didn’t know where to stay or which restaurant to go to, so instead of sifting through pages of online content and reviews, I asked a friend who runs a vineyard in the region. When he gave me his recommendations, I simply booked the places he told me. No questions asked. Were there better places to stay and eat? Potentially. Were there other creative design shops that could have generated equally if not more spectacular logos? More than likely. Do I care? Absolutely not! I love my logo and had a great anniversary outing, and feel secure in my decisions around both because of the feeling I received by selecting recommendations from people I trust. Both experiences are perfect examples of the prescriptive-led sales cycle we spoke about in chapter 2. This means that when it comes to your selling motion, one of the most unobtrusive, empathetic, and authentic ways to convert prospective buyers is simply to surround them with like-minded customers who love you.
David Priemer (Sell the Way You Buy: A Modern Approach To Sales That Actually Works (Even On You!))
Excellence in Statistics: Rigor Statisticians are specialists in coming to conclusions beyond your data safely—they are your best protection against fooling yourself in an uncertain world. To them, inferring something sloppily is a greater sin than leaving your mind a blank slate, so expect a good statistician to put the brakes on your exuberance. They care deeply about whether the methods applied are right for the problem and they agonize over which inferences are valid from the information at hand. The result? A perspective that helps leaders make important decisions in a risk-controlled manner. In other words, they use data to minimize the chance that you’ll come to an unwise conclusion. Excellence in Machine Learning: Performance You might be an applied machine-learning/AI engineer if your response to “I bet you couldn’t build a model that passes testing at 99.99999% accuracy” is “Watch me.” With the coding chops to build both prototypes and production systems that work and the stubborn resilience to fail every hour for several years if that’s what it takes, machine-learning specialists know that they won’t find the perfect solution in a textbook. Instead, they’ll be engaged in a marathon of trial and error. Having great intuition for how long it’ll take them to try each new option is a huge plus and is more valuable than an intimate knowledge of how the algorithms work (though it’s nice to have both). Performance means more than clearing a metric—it also means reliable, scalable, and easy-to-maintain models that perform well in production. Engineering excellence is a must. The result? A system that automates a tricky task well enough to pass your statistician’s strict testing bar and deliver the audacious performance a business leader demands. Wide Versus Deep What the previous two roles have in common is that they both provide high-effort solutions to specific problems. If the problems they tackle aren’t worth solving, you end up wasting their time and your money. A frequent lament among business leaders is, “Our data science group is useless.” And the problem usually lies in an absence of analytics expertise. Statisticians and machine-learning engineers are narrow-and-deep workers—the shape of a rabbit hole, incidentally—so it’s really important to point them at problems that deserve the effort. If your experts are carefully solving the wrong problems, your investment in data science will suffer low returns. To ensure that you can make good use of narrow-and-deep experts, you either need to be sure you already have the right problem or you need a wide-and-shallow approach to finding one.
Harvard Business Review (Strategic Analytics: The Insights You Need from Harvard Business Review (HBR Insights Series))
It’s difficult to imagine that Artificial Intelligence will take the place of people but many believe that it’s only a short time before computers will outthink us. They already can beat our best chess players and have been able to out calculate us since calculators first came onto the scene. IBM’s Watson is on the cutting edge of Cognitive Computers, being used to out think our physicians but closer to home, for the greatest part; our cars are no longer assembled by people but rather robots. Our automobiles can be considered among our first robots, since they took the place of horses. Just after the turn of the last century when the population in the United States crossed the 100 M mark the number of horses came to 20M. Now we have a population of 325 M but only 9 M horses. You might ask what happened. Well back in 1915 there were 2.4 M cars but this jumped to 3.6 M in just one year. Although horses still out-numbered cars the handwriting was on the wall! You might think that this doesn’t apply to us but why not? The number of robots increase, taking the place of first our workers on the assembly line and then workers in the food industry and this takes us from tractors and combines on the farms to the cooking and serving hamburgers at your favorite burger joint. People are becoming redundant! That’s right we are becoming superfluous! Worldwide only 7 out of 100 people have college degrees and here in the United States only 40% of our working population possesses a sheep skin, although mine is printed on ordinary paper. With education becoming ever more expensive, we as a population are becoming ever more uneducated. A growing problem is that as computers and robots become smarter, as they are, we are no longer needed to be anything more than a consumer and where will the money come from for that? I recently read that this death spiral will run its course within 40 years! Nice statistics that we’re looking at…. Looking at the bright side of things you can now buy an atomically correct, life sized doll, as perhaps a robotic non-complaining, companion for under $120. In time these robotic beings will be able to talk back but hopefully there will be an off switch. As interesting as this sounds it will most likely not be for everyone, however it may appeal to some of our less capable, not to have to actually interface with real live people. The fact is that most people will soon outlive their usefulness! We as a society are being challenged and there will soon be little reason for our being. When machines make machines that can out think us; when we become dumb and superfluous, then what? Are we ready for this transition? It’s scary but If nothing else, it’s something to think about….
Hank Bracker
Not a single girl among those she circumcised died, and she appeared quite proud of that statistic. That it was grisly and disfiguring—no matter how nicely she did it—didn’t seem to occur to her.
Elizabeth George (Something to Hide (Inspector Lynley, #21))
wonderfully clarifying. Looking at the Global Wealth Report from Credit Suisse, the source of Oxfam’s claims, we can play with some of those numbers to shed more light on the topic.[*] Forty-two million people have more than a million dollars each, collectively owning about $142 trillion. A few of them are billionaires, but most are not. If you have a nice house with no mortgage in a place such as London, New York, or Tokyo, that might easily be enough to put you in this group. So would the right to a good private pension.[*] [19] Nearly 1 percent of the world’s adult population are in this group.
Tim Harford (The Data Detective: Ten Easy Rules to Make Sense of Statistics)
There are statistics out there that point to fifty percent of books in one’s local bookstore being ghostwritten. That seems high to me, so let’s halve that, and we still have a nice twenty-five percent.
Natalie Barelli (After He Killed Me (Emma Fern, #2))
In the final analysis, the relation of the individual to society must not be conceived after the atomistic and mechanistic pattern of bourgeois individualism which destroys the organic social totality, or after the biological and animal pattern of the statist or racist totalitarian conception which swallows up the person, here reduced to a mere histological element of Behemoth or Leviathan, in the body of the state, or after the biological and industrial pattern of the Communistic conception which ordains the entire person, like a worker in the great human hive, to the proper work of the social whole. The relation of the individual to society must be conceived after an irreducibly human and specifically ethicosocial pattern, that is, personalist and communalist at the same time; the organization to be accomplished is one of liberties. But an organization of liberty is is unthinkable apart from the amoral realities of justice and civil amity, which, on the natural and temporal plane, correspond to what the Gospel calls brotherly love on the spiritual and supernatural plane. This brings us back to our considerations of the manner in which the paradox of social life is resolved in a progressive movement that will never be terminated here-below. There is a common work to be accomplished by the social whole as such. This whole, of which human person are the parts, is not ‘neutral’ but is itself committed and bound by a temporal vocation. Thus the persons are subordinated to this common work. Nevertheless, not only in the political order, is it essential to the common good to flow back upon the persons, but also in another order where that which is most profound in the person, its supra-temporal vocation and the goods connected with it, is a transcendent end, it is essential that society itself and its common work are indirectly subordinated. This follows from the fact that the principal value of the common work of society is the freedom of expansion of the person together with all the guarantees which this freedom implies and the diffusion of good that flows from it. In short, the political common good is a common good of human persons. And thus it turns out that, in subordinating oneself to this common work, by the grace of justice and amity, each one of us is trill subordinated to the good of persons, to the accomplishment of the personal life of others an, at the same time, to the interior dignity of ones own person. But for this solution to be practical, there must be full recognition in the city of the true nature of the common work and, at the same time, recognition also of the importance and political worth--so nicely perceived by Aristotle--of the virtue of amity.
Jacques Maritain (Person and the Common Good)
Here’s an example: DNA stores information very nicely, in a durable format that allows for exact duplication. A ribosome turns that stored information into a sequence of amino acids, a protein, which folds up into a variety of chemically active shapes. The combined system, DNA and ribosome, can build all sorts of protein machinery. But what good is DNA, without a ribosome that turns DNA information into proteins? What good is a ribosome, without DNA to tell it which proteins to make? Organisms don’t always leave fossils, and evolutionary biology can’t always figure out the incremental pathway. But in this case we do know how it happened. RNA shares with DNA the property of being able to carry information and replicate itself, although RNA is less durable and copies less accurately. And RNA also shares the ability of proteins to fold up into chemically active shapes, though it’s not as versatile as the amino acid chains of proteins. Almost certainly, RNA is the single A which predates the mutually dependent A* and B. It’s just as important to note that RNA does the combined job of DNA and proteins poorly, as that it does the combined job at all. It’s amazing enough that a single molecule can both store information and manipulate chemistry. For it to do the job well would be a wholly unnecessary miracle. What was the very first replicator ever to exist? It may well have been an RNA strand, because by some strange coincidence, the chemical ingredients of RNA are chemicals that would have arisen naturally on the prebiotic Earth of 4 billion years ago. Please note: evolution does not explain the origin of life; evolutionary biology is not supposed to explain the first replicator, because the first replicator does not come from another replicator. Evolution describes statistical trends in replication. The first replicator wasn’t a statistical trend, it was a pure accident. The notion that evolution should explain the origin of life is a pure strawman—more creationist misrepresentation.
Eliezer Yudkowsky (Rationality: From AI to Zombies)
Paglia has written movingly about her days teaching Shakespeare and Sophocles to factory workers at the Sikorsky Aircraft plant outside New Have, so she could hardly endorse a solution that involves ejecting so many working-class students from college life. When you ask the average humanities professor whether too many unready students might not be getting hustled into the matriculation office, he (or, statistically, she) will often wax populist: everyone deserves a chance to contemplate the big questions of life. Such big questions are indeed the stuff of literature and philosophy. But they are also the stuff of church. Religion has historically been the place where classes below the upper middle can air their ideas about meaning and seek to integrate them with a greater tradition. The nice thing about church is, it doesn’t cost $50,000 a year.
Helen Andrews (Boomers: The Men and Women Who Promised Freedom and Delivered Disaster)