100 Random Quotes

Another mistaken notion connected with the law of large numbers is the idea that an event is more or less likely to occur because it has or has not happened recently. The idea that the odds of an event with a fixed probability increase or decrease depending on recent occurrences of the event is called the gambler's fallacy. For example, if Kerrich landed, say, 44 heads in the first 100 tosses, the coin would not develop a bias towards the tails in order to catch up! That's what is at the root of such ideas as "her luck has run out" and "He is due." That does not happen. For what it's worth, a good streak doesn't jinx you, and a bad one, unfortunately , does not mean better luck is in store.

In my illustrious career as a university student, I turned in over 100 papers so that one day, in the end, I got 1 paper in return.

In medical school, doctors are taught to view the human body as a random mistake-ridden vessel that has to be forced into submission with surgery, antibiotics, antihypertensives, antihistamines, anti-inflammatories, and other medical interventions. The natural extension of this paradigm over the past 100 years has been for the medical profession to condition human beings not to trust anyone but certified medical doctors to fix these defective aberrations of creation.

The system is not your friend. The system is not your enemy. The system is a retarded giant throwing wads of $100 bills and books of rules in random directions while shouting “LOOK AT ME! I’M HELPING! I’M HELPING!” Sometimes by luck you catch a wad of cash, and you think the system loves you. Other times by misfortune you get hit in the gut with a rulebook, and you think the system hates you. But either one is giving the system too much credit.

In the 1990s, the ratio of buy to sell recommendations climbed to 100 to 1, particularly for brokerage firms with large investment banking businesses.

It is far better to have 10,000 Facebook friends who are in the same category or aligned with your values or a common inter- est than 100,000 random robot followers from around the world.

A statement: children who watch violent TV programmes tend to be more violent when they grow up. But did the TV cause the violence, or do violent children preferentially enjoy watching violent programmes? Very likely both are true. Commercial defenders of TV violence argue that anyone can distinguish between television and reality. But Saturday morning children’s programmes now average 25 acts of violence per hour. At the very least this desensitizes young children to aggression and random cruelty. And if impressionable adults can have false memories implanted in their brains, what are we implanting in our children when we expose them to some 100,000 acts of violence before they graduate from elementary school?

In fact, when some wedding guest inevitably complains about the seating arrangements, you might point out how long it would have taken you to consider every possibility: assuming you spent one second considering each one, it would come to more than half a million years. The unhappy guest will assume, of course, that you are being histrionic.

A stock selling at $100 per share with earnings of $10 per share would have the same P/E multiple (10) as a stock selling at $40 with earnings of $4 per share. It is the P/E multiple, not the price, that really tells you how a stock is valued in the market.

With diversity things I know people are always like, "oh don't force diversity!" I'm of the opinion that if I read your book and at the end of the book I'm like, it's kinda weird that there are no black people in this? Bad world building. The way I find easiest to explain especially to people who are maybe hesitant to change, is that if you took 100 random people off of the earth and were like "here's 100 people!" The chances of every single one of them being pale white and straight are very unlikely. So when I read a book and you introduce me to over a hundred characters and every single one of them is pale white and straight, it's bad world building. It doesn't feel like a real place to me.

Thanks largely to the attempts to integrate women into the armed forces of many modern countries, the physical differences between the sexes have been precisely measured.[296] One study found the average U.S. Army female recruit to be 12 centimeters shorter and 14.3 kilograms lighter than her male brethren. Compared to the average male recruit, females had 16.9 fewer kilograms of muscle and 2.6 more kilograms of fat, as well as 55 percent of the upper body strength and 72 percent of the lower body strength. Fat mass is inversely related to aerobic capacity and heat tolerance, hence women are also at a disadvantage when performing activities such as carrying heavy loads, working in the heat and running. Even when the samples were controlled for height, women possessed only 80 percent of the overall strength of men. Only the upper 20 percent of women could do as well physically as the lower 20 percent of men. Had the 100 strongest individuals out of a random group consisting of 100 men and 100 women been selected, 93 would be male and only seven female.[297] Yet another study showed gthat only the upper 5 percent of women are as strong as the median male.[298]

Kahneman and Tversky concluded that losses were 2½ times as undesirable as equivalent gains were desirable. In other words, a dollar loss is 2½ times as painful as a dollar gain is pleasurable. People exhibit extreme loss aversion, even though a change of $100 of wealth would hardly be noticed for most people with substantial assets. We’ll see later how loss aversion leads many investors to make costly mistakes.

One of the most popular TED Talks came from Jia Jiang, in which he spoke about spending time living outside of his comfort zone. Jiang spent 100 days seeking out opportunities to experience rejection to help him overcome social anxiety and his fear of rejection to become a more confident person. It involved him doing things like asking a random stranger to lend him $100, knocking on someone’s door and asking to play soccer in their backyard, and asking for second helpings in a restaurant without paying. At the end of the 100 days, Jiang was a completely different person—he was confident and sociable because of how kind people were to him during this time spent outside his comfort zone.

With Britain preoccupied by World War II and the United States not yet in it, the quest to produce bulk penicillin moved to a U.S. government research facility in Peoria, Illinois. Scientists and other interested parties all over the Allied world were secretly asked to send in soil and mold samples. Hundreds responded, but nothing they sent proved promising. Then, two years after testing had begun, a lab assistant in Peoria named Mary Hunt brought in a cantaloupe from a local grocery store. It had a “pretty golden mold” growing on it, she recalled later. That mold proved to be two hundred times more potent than anything previously tested. The name and location of the store where Mary Hunt shopped are now forgotten, and the historic cantaloupe itself was not preserved: after the mold was scraped off, it was cut into pieces and eaten by the staff. But the mold lived on. Every bit of penicillin made since that day is descended from that single random cantaloupe. Within a year, American pharmaceutical companies were producing 100 billion units of penicillin a month.

In the EPJ results, there were two statistically distinguishable groups of experts. The first failed to do better than random guessing, and in their longer-range forecasts even managed to lose to the chimp. The second group beat the chimp, though not by a wide margin, and they still had plenty of reason to be humble. Indeed, they only barely beat simple algorithms like “always predict no change” or “predict the recent rate of change.” Still, however modest their foresight was, they had some. So why did one group do better than the other? It wasn’t whether they had PhDs or access to classified information. Nor was it what they thought—whether they were liberals or conservatives, optimists or pessimists. The critical factor was how they thought. One group tended to organize their thinking around Big Ideas, although they didn’t agree on which Big Ideas were true or false. Some were environmental doomsters (“We’re running out of everything”); others were cornucopian boomsters (“We can find cost-effective substitutes for everything”). Some were socialists (who favored state control of the commanding heights of the economy); others were free-market fundamentalists (who wanted to minimize regulation). As ideologically diverse as they were, they were united by the fact that their thinking was so ideological. They sought to squeeze complex problems into the preferred cause-effect templates and treated what did not fit as irrelevant distractions. Allergic to wishy-washy answers, they kept pushing their analyses to the limit (and then some), using terms like “furthermore” and “moreover” while piling up reasons why they were right and others wrong. As a result, they were unusually confident and likelier to declare things “impossible” or “certain.” Committed to their conclusions, they were reluctant to change their minds even when their predictions clearly failed. They would tell us, “Just wait.” The other group consisted of more pragmatic experts who drew on many analytical tools, with the choice of tool hinging on the particular problem they faced. These experts gathered as much information from as many sources as they could. When thinking, they often shifted mental gears, sprinkling their speech with transition markers such as “however,” “but,” “although,” and “on the other hand.” They talked about possibilities and probabilities, not certainties. And while no one likes to say “I was wrong,” these experts more readily admitted it and changed their minds. Decades ago, the philosopher Isaiah Berlin wrote a much-acclaimed but rarely read essay that compared the styles of thinking of great authors through the ages. To organize his observations, he drew on a scrap of 2,500-year-old Greek poetry attributed to the warrior-poet Archilochus: “The fox knows many things but the hedgehog knows one big thing.” No one will ever know whether Archilochus was on the side of the fox or the hedgehog but Berlin favored foxes. I felt no need to take sides. I just liked the metaphor because it captured something deep in my data. I dubbed the Big Idea experts “hedgehogs” and the more eclectic experts “foxes.” Foxes beat hedgehogs. And the foxes didn’t just win by acting like chickens, playing it safe with 60% and 70% forecasts where hedgehogs boldly went with 90% and 100%. Foxes beat hedgehogs on both calibration and resolution. Foxes had real foresight. Hedgehogs didn’t.

Hamilton Pool, which is located near Austin, is one of the most remarkable sights of nature to be observed in Texas. It’s a natural spring that’s situated in limestone bedrock. Its water comes from an underground river. There’s a deep overhang in one of the walls of the cavern that’s of much interest to visitors. Over 100 years ago, the Hamilton Pool was completely covered by a dome that later collapsed. The Hamilton Pool is one of Texas’s many tourist attractions.

There is no such thing as randomness. No one who could detect every force operating on a pair of dice would ever play dice games, because there would never be any doubt about the outcome. The randomness, such as it is, applies to our ignorance of the possible outcomes. It doesn’t apply to the outcomes themselves. They are 100% determined and are not random in the slightest. Scientists have become so confused by this that they now imagine that things really do happen randomly, i.e. for no reason at all.

Science is just an enormous system of non explanation since randomness does not belong to the category of explanation. It’s a category error to believe it does. Randomness inhabits the same category as magic, miracles, mystery, faith, prayer, and the “will of God”. It has no explanatory power whatsoever and is 100% contradicted by the principle of sufficient reason.

Don’t be fooled. Store bought 100% "real" orange juice is 100% artificially flavored.

Per your mission instructions, you can reject the null hypothesis that this bus contains a random sample of 60 Changing Lives study participants at the .05 significance level. This means (1) the mean weight on the bus falls into a range that we would expect to observe only 5 times in 100 if the null hypothesis were true and this were really a bus full of Changing Lives passengers; (2) you can reject the null hypothesis at the .05 significance level; and (3) on average, 95 times out of 100 you will have correctly rejected the null hypothesis, and 5 times out of 100 you will be wrong, meaning that you have concluded that this is not a bus of Changing Lives participants, when in fact it is. This sample of Changing Lives folks just happens to have a mean weight that is particularly high or low relative to the mean for the study participants overall.

Here is an exercise that I do with my students to make the same basic point. The larger the class, the better it works. I ask everyone in the class to take out a coin and stand up. We all flip the coin; anyone who flips heads must sit down. Assuming we start with 100 students, roughly 50 will sit down after the first flip. Then we do it again, after which 25 or so are still standing. And so on. More often than not, there will be a student standing at the end who has flipped five or six tails in a row. At that point, I ask the student questions like “How did you do it?” and “What are the best training exercises for flipping so many tails in a row?” or “Is there a special diet that helped you pull off this impressive accomplishment?” These questions elicit laughter because the class has just watched the whole process unfold; they know that the student who flipped six tails in a row has no special coin-flipping talent. He or she just happened to be the one who ended up with a lot of tails. When we see an anomalous event like that out of context, however, we assume that something besides randomness must be responsible.

BUSTER You may have heard the myth that higher-protein diets lead to kidney dysfunction. The data tell us otherwise. A meta-analysis conducted by prominent protein researcher Stu Philips looked at higher-protein (HP) diets (≥ 1.5 g/kg body weight or ≥ 20% energy intake or ≥ 100 g/day) and their effects on kidney function. The indicator known as glomerular filtration rate (GFR) reflects any change in the efficiency of kidney function. When compared with normal- or lower-protein (≥ 5% less energy intake from protein/day) diets, HP diet interventions did not significantly elevate GFR relative to diets containing lower amounts of protein. Researchers concluded that HP intake does not negatively influence renal function in healthy adults.2 A systematic review of randomized controlled trials and epidemiologic studies conducted by Van Elswyk et al. found that HP intake (≥ 20% but < 35% of energy or ≥ 10% higher than a comparison intake) had little to no effect on blood markers of kidney function (e.g., blood pressure) when compared with groups following US RDA recommendations (0.8 g/kg or 10–15% of energy).

However, most of you reading this have been trained by society, your parents, and your friends to be helpless, so you spend your free time lying on the couch, watching TV, sliding into depression—instead of actively placing bets on your future. You’ve been hoodwinked. Bamboozled. Fooled. Slipped the mickey. Fallen for it. You’re the sucker at the table who doesn’t realize that you’re just another monthly subscription, and random ad clicker, for corporate America.

In Argentina, for example, in the summer of 2020, Dr. Hector Carvallo conducted a randomized placebo-controlled trial of ivermectin as a preventative, finding 100 percent efficacy against COVID.

In Argentina, for example, in the summer of 2020, Dr. Hector Carvallo conducted a randomized placebo-controlled trial of ivermectin as a preventative, finding 100 percent efficacy against COVID. Carvallo’s team found no infections among the 788 workers who took weekly ivermectin prophylaxis, whereas 58 percent of the 407 controls had become ill with COVID-19.

The self-destruction of a group always follows the same patterns. You only need to introduce some viruses to the group and poof, it’s all gone. These viruses come in the form of very ignorant narcissists that nobody has the courage to kick off of the group. Quite often, the group even promotes itself as being against the personalities that are in front of their eyes every day, people they praise and even lead them. And well, that’s how you know a group is truly finished. Scientology is a very interesting example of this, because of how clear their books are. For example, they claim to love artists but end up insulting real artists. Scientologists are so obsessed with being perceived as artists, that they downgrade real art in the process. You have many scientologists, for example, that think splashing a random amount of ink into a white board is art. They all want to be artists, and that’s fine, but they are too lazy to see how real art is made, and so, they downgrade the value of art. And in doing this, they actually distort the meaning of art and decrease the value of the real artists. And so, a group that promotes itself as being uplifting and positive, ends up being offensive and destructive. They have all these books on moral codes and moral behavior, and dozens of courses on the same topic, and if you report a scientologist for criminal behavior, they ignore you and deem you an attacker of the group. And there goes the level of sanity of this group down the scale, while they themselves invert the scale and tell you the opposite story. It would be like looking at your mental health through someone suffering with poor mental health. They are as aware of what I am saying as any mentally ill person is aware of his mental illnesses. If anyone confronts them with the facts, they themselves get offended, and then proceed to attack, because that’s what they think their founder told them to do. Except that the founder was talking about attacking insanity and not people. In other words, they should use these facts to look further into their books and their own misinterpretations, and which they don’t. Those people that splash random colors into a white board, will then tell you, the one who has been using techniques, and winning awards, and creating something unique, that you don’t understand art. They remind me of the writers with one book that doesn't sell, trying to tell me how they are better than me, with more than 100 books in best selling charts. How delusional, arrogant and stupid has one to be to not see this? The level of awareness of such individual is comparable to a drunk person going to a Jujitsu dojo, asking the instructor to fight him because he is convinced he can beat anyone with all that alcohol in his head. That, however, is not the cherry on top of the cake. The cherry on top of the cake, is when a religious group listens to a psychopath talking against psychopaths. You can write many academic papers on this topic and never reach a conclusion, because it's really hard to make conclusions on stupidity. So what’s wrong with religion? Why are some religious groups persecuted and attacked? The answer to these questions isn’t as relevant as what we can observe people doing, when denying the most obvious writings, inverting them and distorting the meanings. Christians have already mastered this art.

The first few primes are 2, 3, 5, 7, 11 and 13—but despite their simple definition the prime numbers appear to be scattered randomly amid the integers.

The Random Book Club is an offshoot of the shop which I set up a few years ago when business was sore and the future looked bleak. For £59 a year subscribers receive a book a month, but they have no say over what genre of book they receive, and quality control is entirely down to me. I am extremely judicious in what I choose to put in the box from which the RBC books are parcelled and sent. Since subscribers are clearly inveterate readers, I always take care to pick books that I think anyone who loves reading for its own sake would enjoy. There is nothing that would require too much technical expertise to understand: a mix of fiction and non-fiction, with the weight slightly towards non-fiction, and some poetry. Among the books going out later this month are a copy of Clive James’s Other Passports, Lawrence Durrell’s Prospero’s Cell, Iris Murdoch’s biography of Sartre, Neville Shute’s A Town Like Alice, and a book called 100+ Principles of Genetics. All the books are in good condition, none is ex-library, and some – several of them each year – are hundreds of years old. I estimate that if the members decided to sell the books on eBay, they would more than make their money back. There is a forum on the web site, but nobody uses it, which gives me an insight into the type of person who is attracted to the idea – they don’t like clubs where they have to interact with other people. Perhaps that is why I came up with the idea in the first place – it is a sort of Groucho Marx approach to clubs. There are about 150 members and, apart from a minimal amount of advertising in the Literary Review, the only marketing I do is to have a web site and Facebook page, neither of which I have updated for some time. Word of mouth seems to have been the best way of marketing it. It has saved me from financial embarrassment during a very difficult time in the book trade.

By contrast, AlphaGo Zero was not given any human information about Go except for the rules of the game, and after about three days of playing against itself, it evolved from making random moves to easily defeating its previous human-trained incarnation, AlphaGo, by 100 games to 0.[81]

His favorite book was A Million Random Digits with 100,000 Normal Deviates,

The average American 3-year old can recognize about 100 brand logos.

The term was popularized by former Wall Street trader Nassim Nicholas Taleb, who wrote about the concept in his 2001 book Fooled by Randomness.

The electronics effort faced even greater challenges. To launch that category, David Risher tapped a Dartmouth alum named Chris Payne who had previously worked on Amazon’s DVD store. Like Miller, Payne had to plead with suppliers—in this case, Asian consumer-electronics companies like Sony, Toshiba, and Samsung. He quickly hit a wall. The Japanese electronics giants viewed Internet sellers like Amazon as sketchy discounters. They also had big-box stores like Best Buy and Circuit City whispering in their ears and asking them to take a pass on Amazon. There were middlemen distributors, like Ingram Electronics, but they offered a limited selection. Bezos deployed Doerr to talk to Howard Stringer at Sony America, but he got nowhere. So Payne had to turn to the secondary distributors—jobbers that exist in an unsanctioned, though not illegal, gray market. Randy Miller, a retail finance director who came to Amazon from Eddie Bauer, equates it to buying from the trunk of someone’s car in a dark alley. “It was not a sustainable inventory model, but if you are desperate to have particular products on your site or in your store, you do what you need to do,” he says. Buying through these murky middlemen got Payne and his fledgling electronics team part of the way toward stocking Amazon’s virtual shelves. But Bezos was unimpressed with the selection and grumpily compared it to shopping in a Russian supermarket during the years of Communist rule. It would take Amazon years to generate enough sales to sway the big Asian brands. For now, the electronics store was sparely furnished. Bezos had asked to see $100 million in electronics sales for the 1999 holiday season; Payne and his crew got about two-thirds of the way there. Amazon officially announced the new toy and electronics stores that summer, and in September, the company held a press event at the Sheraton in midtown Manhattan to promote the new categories. Someone had the idea that the tables in the conference room at the Sheraton should have piles of merchandise representing all the new categories, to reinforce the idea of broad selection. Bezos loved it, but when he walked into the room the night before the event, he threw a tantrum: he didn’t think the piles were large enough. “Do you want to hand this business to our competitors?” he barked into his cell phone at his underlings. “This is pathetic!” Harrison Miller, Chris Payne, and their colleagues fanned out that night across Manhattan to various stores, splurging on random products and stuffing them in the trunks of taxicabs. Miller spent a thousand dollars alone at a Toys “R” Us in Herald Square. Payne maxed out his personal credit card and had to call his wife in Seattle to tell her not to use the card for a few days. The piles of products were eventually large enough to satisfy Bezos, but the episode was an early warning. To satisfy customers and their own demanding boss during the upcoming holiday, Amazon executives were going to have to substitute artifice and improvisation for truly comprehensive selection.

Seemingly random events and movements and trends gather power and momentum, unseen for long periods of time, and then bursting to the surface unexpectedly. It will, of course, all make sense in 100 years as the historians mull through it. But for those of us in the moment, we are often forced to ask, “What the hell is going on here?

look no further than Peter A. Lawrence’s developmental biology text The Making of a Fly, which in April 2011 was selling for $23,698,655.93 (plus $3.99 shipping) on Amazon’s third-party marketplace. How and why had this—admittedly respected—book reached a sale price of more than $23 million? It turns out that two of the sellers were setting their prices algorithmically as constant fractions of each other: one was always setting it to 0.99830 times the competitor’s price, while the competitor was automatically setting their own price to 1.27059 times the other’s. Neither seller apparently thought to set any limit on the resulting numbers, and eventually the process spiraled totally out of control. It’s possible that a similar mechanism was in play during the enigmatic and controversial stock market “flash crash” of May 6, 2010, when, in a matter of minutes, the price of several seemingly random companies in the S&P 500 rose to more than $100,000 a share, while others dropped precipitously—sometimes to $0.01 a share. Almost $1 trillion of value instantaneously went up in smoke.

The first basic income pilot in a developing country was implemented in the small Namibian village of Otjivero-Omitara in 2008–9, covering about 1,000 people.40 The study was carried out by the Namibian Basic Income Grant Coalition, with money raised from foundations and individual donations. Everyone in the village, including children but excluding over-sixties already receiving a social pension, was given a very small basic income of N$100 a month (worth US$12 at the time or about a third of the poverty line), and the outcomes compared with the previous situation. The results included better nutrition, particularly among children, improved health and greater use of the local primary healthcare centre, higher school attendance, increased economic activity and enhanced women’s status.41 The methodology would not have satisfied those favouring randomized control trials that were coming into vogue at the time. No control village was chosen to allow for the effects of external factors, in the country or economy, because those directing the pilot felt it was immoral to impose demands, in the form of lengthy surveys, on people who were being denied the benefit of the basic income grants. However, there were no reported changes in policy or outside interventions during the period covered by the pilot, and confidence in the results is justified both by the observed behaviour, and by recipients’ opinions in successive surveys. School attendance went up sharply, though there was no pressure on parents to send their children to school. The dynamics were revealing. Although the primary school was a state school, parents were required to pay a small fee for each child. Before the pilot, registration and attendance were low, and the school had too little income from fees to pay for basics, which made the school unattractive and lowered teachers’ morale. Once the cash transfers started, parents had enough money to pay school fees, and teachers had money to buy paper, pens, books, posters, paints and brushes, making the school more attractive to parents and children and raising the morale and, probably, the capacity of its teachers. There was also a substantial fall in petty economic crime such as stealing vegetables and killing small livestock for food. This encouraged villagers to plant more vegetables, buy more fertilizer and rear more livestock. These dynamic community-wide economic effects are usually overlooked in conventional evaluations, and would not be spotted if cash was given only to a random selection of individuals or households and evaluated as a randomized control trial. Another outcome, unplanned and unanticipated, was that villagers voluntarily set up a Basic Income Advisory Committee, led by the local primary school teacher and the village nurse, to advise people on how to spend or save their basic income money. The universal basic income thus induced collective action, and there was no doubt that this community activism increased the effectiveness of the basic incomes.

80. Every day in the USA people consume approximately 100 acres (0.4 square kilometers) of pizza.

While a ten-year survival rate for a trader is in the single digits, that of a risk manager is close to 100%).

I am convinced that there exists a tradable security in the Western world that would be 100% correlated with the changes in temperature in Ulan Bator,

PayPal’s big challenge was to get new customers. They tried advertising. It was too expensive. They tried BD [business development] deals with big banks. Bureaucratic hilarity ensued. … the PayPal team reached an important conclusion: BD didn’t work. They needed organic, viral growth. They needed to give people money. So that’s what they did. New customers got $10 for signing up, and existing ones got $10 for referrals. Growth went exponential, and PayPal wound up paying $20 for each new customer. It felt like things were working and not working at the same time; 7 to 10 percent daily growth and 100 million users was good. No revenues and an exponentially growing cost structure were not. Things felt a little unstable. PayPal needed buzz so it could raise more capital and continue on. (Ultimately, this worked out. That does not mean it’s the best way to run a company. Indeed, it probably isn’t.)2 Thiel’s account captures both the desperation of those early days and the almost random experimentation the company resorted to in an effort to get PayPal off the ground. But in the end, the strategy worked. PayPal dramatically increased its base of consumers by incentivizing new sign-ups. Most important, the PayPal team realized that getting users to sign up wasn’t enough; they needed them to try the payment service, recognize its value to them, and become regular users. In other words, user commitment was more important than user acquisition. So PayPal designed the incentives to tip new customers into the ranks of active users. Not only did the incentive payments make joining PayPal feel riskless and attractive, they also virtually guaranteed that new users would start participating in transactions—if only to spend the $10 they’d been gifted in their accounts. PayPal’s explosive growth triggered a number of positive feedback loops. Once users experienced the convenience of PayPal, they often insisted on paying by this method when shopping online, thereby encouraging sellers to sign up. New users spread the word further, recommending PayPal to their friends. Sellers, in turn, began displaying PayPal logos on their product pages to inform buyers that they were prepared to honor this method of online payment. The sight of those logos informed more buyers of PayPal’s existence and encouraged them to sign up. PayPal also introduced a referral fee for sellers, incentivizing them to bring in still more sellers and buyers. Through these feedback loops, the PayPal network went to work on its own behalf—it served the needs of users (buyers and sellers) while spurring its own growth.

Take a population of 10 people, 9 having a net worth of $ 30,000 and 1 having a net worth of $ 1,000. The average net worth is $ 27,100 and 9 out of 10 people will have above average wealth.

Q. If you were president what wold you do? A. declare world peace, remove taxation on all people with income under 100K place qa 20% flat tax on all others with zero loopholes, forgive all student loans and make state colleges tuition free, repair the infrastructure and phase out all industries that contribute to pollution or climate change. Care for all animals and their habitats. Abolish money in politics remove gerrymandering and the electoral college. Remove all non-violent offenders from prisons to create an infra-structure job corps release program and things like that....

571. About 100 years ago, sea level was 7 inches (18 cm) lower.

Call a random number and say "Get ready because I am". Call back in 10 minutes and ask if they are ready yet.

I. DARE. YOU. To wear no makeup for a whole day. To bake brownies, then eat them. As many as you want. To tell the bitch how you really feel. To laugh hysterically and not worry if your face looks stupid. To run around a field with your friends and just scream. To cry, and let it all out. To kiss your crush, randomly. To speak your mind in class. Question the rules. One day, I dare you to be 100% you.

Don, a CPA, kept a log of the first 100 horses that had been touted by insiders as ready to win and absolutely good things. Exactly six had won. We could have done as well by consulting a table of random numbers.

The breakthrough came in the early 1980s, when Judea Pearl, a professor of computer science at the University of California, Los Angeles, invented a new representation: Bayesian networks. Pearl is one of the most distinguished computer scientists in the world, his methods having swept through machine learning, AI, and many other fields. He won the Turing Award, the Nobel Prize of computer science, in 2012. Pearl realized that it’s OK to have a complex network of dependencies among random variables, provided each variable depends directly on only a few others. We can represent these dependencies with a graph like the ones we saw for Markov chains and HMMs, except now the graph can have any structure (as long as the arrows don’t form closed loops). One of Pearl’s favorite examples is burglar alarms. The alarm at your house should go off if a burglar attempts to break in, but it could also be triggered by an earthquake. (In Los Angeles, where Pearl lives, earthquakes are almost as frequent as burglaries.) If you’re working late one night and your neighbor Bob calls to say he just heard your alarm go off, but your neighbor Claire doesn’t, should you call the police? Here’s the graph of dependencies: If there’s an arrow from one node to another in the graph, we say that the first node is a parent of the second. So Alarm’s parents are Burglary and Earthquake, and Alarm is the sole parent of Bob calls and Claire calls. A Bayesian network is a graph of dependencies like this, together with a table for each variable, giving its probability for each combination of values of its parents. For Burglary and Earthquake we only need one probability each, since they have no parents. For Alarm we need four: the probability that it goes off even if there’s no burglary or earthquake, the probability that it goes off if there’s a burglary and no earthquake, and so on. For Bob calls we need two probabilities (given alarm and given no alarm), and similarly for Claire. Here’s the crucial point: Bob calling depends on Burglary and Earthquake, but only through Alarm. Bob’s call is conditionally independent of Burglary and Earthquake given Alarm, and so is Claire’s. If the alarm doesn’t go off, your neighbors sleep soundly, and the burglar proceeds undisturbed. Also, Bob and Claire are independent given Alarm. Without this independence structure, you’d need to learn 25 = 32 probabilities, one for each possible state of the five variables. (Or 31, if you’re a stickler for details, since the last one can be left implicit.) With the conditional independencies, all you need is 1 + 1 + 4 + 2 + 2 = 10, a savings of 68 percent. And that’s just in this tiny example; with hundreds or thousands of variables, the savings would be very close to 100 percent.

Imagine 100 book bags, each of which contains 1,000 poker chips. Forty-five bags contain 700 black chips and 300 red chips. The other 55 bags contain 300 black chips and 700 red chips. You cannot see inside any of the bags. One of the bags is selected at random by means of a coin toss. Consider the following two questions about the selected book bag. 1. What probability would you assign to the event that the selected bag contains predominantly black chips? 2. Now imagine that 12 chips are drawn, with replacement, from the selected bag. These twelve draws produce 8 blacks and 4 reds. Would you use the new information about the drawing of chips to revise your probability that the selected bag contains predominantly black chips? If so, what new probability would you assign?

it is not uncommon for experts in DNA analysis to testify at a criminal trial that a DNA sample taken from a crime scene matches that taken from a suspect. How certain are such matches? When DNA evidence was first introduced, a number of experts testified that false positives are impossible in DNA testing. Today DNA experts regularly testify that the odds of a random person’s matching the crime sample are less than 1 in 1 million or 1 in 1 billion. With those odds one could hardly blame a juror for thinking, throw away the key. But there is another statistic that is often not presented to the jury, one having to do with the fact that labs make errors, for instance, in collecting or handling a sample, by accidentally mixing or swapping samples, or by misinterpreting or incorrectly reporting results. Each of these errors is rare but not nearly as rare as a random match. The Philadelphia City Crime Laboratory, for instance, admitted that it had swapped the reference sample of the defendant and the victim in a rape case, and a testing firm called Cellmark Diagnostics admitted a similar error.20 Unfortunately, the power of statistics relating to DNA presented in court is such that in Oklahoma a court sentenced a man named Timothy Durham to more than 3,100 years in prison even though eleven witnesses had placed him in another state at the time of the crime. It turned out that in the initial analysis the lab had failed to completely separate the DNA of the rapist and that of the victim in the fluid they tested, and the combination of the victim’s and the rapist’s DNA produced a positive result when compared with Durham’s. A later retest turned up the error, and Durham was released after spending nearly four years in prison.21 Estimates of the error rate due to human causes vary, but many experts put it at around 1 percent. However, since the error rate of many labs has never been measured, courts often do not allow testimony on this overall statistic. Even if courts did allow testimony regarding false positives, how would jurors assess it? Most jurors assume that given the two types of error—the 1 in 1 billion accidental match and the 1 in 100 lab-error match—the overall error rate must be somewhere in between, say 1 in 500 million, which is still for most jurors beyond a reasonable doubt. But employing the laws of probability, we find a much different answer. The way to think of it is this: Since both errors are very unlikely, we can ignore the possibility that there is both an accidental match and a lab error. Therefore, we seek the probability that one error or the other occurred. That is given by our sum rule: it is the probability of a lab error (1 in 100) + the probability of an accidental match (1 in 1 billion). Since the latter is 10 million times smaller than the former, to a very good approximation the chance of both errors is the same as the chance of the more probable error—that is, the chances are 1 in 100. Given both possible causes, therefore, we should ignore the fancy expert testimony about the odds of accidental matches and focus instead on the much higher laboratory error rate—the very data courts often do not allow attorneys to present! And so the oft-repeated claims of DNA infallibility are exaggerated.

For example, in many developing countries, children, and in particular girls, do not spend enough time at school, even when school is free, to learn as they should. To change this, the following strategies have been suggested: •Unconditional cash transfers for girls; •Cash transfers for girls, conditional on attendance; •Merit scholarships for girls; •Free primary school uniforms; •Deworming through primary schools; •Providing information to parents about the increased wages of those who stay at school. All of these strategies look plausible. When resources for education are scarce, as they always are, especially in developing countries, which one should be tried? In the absence of randomized testing, it would be impossible to know. But the Jameel Poverty Action Lab has tested them and found that the last one on the list is by far the most cost-effective. Every $100 spent on providing information to parents about the increased wages of those who stay at school results in an amazing 20.7 additional years spent at school! Deworming through primary schools is also highly cost-effective, leading to 13.9 additional years spent at school per $100 spent. Of the remaining interventions, the first two are relatively ineffective, both gaining less than 1 additional year per $100, and the cash transfers, whether conditional or unconditional, gain less than one-tenth of an additional year per $100.8 The most effective method thus results in more than two hundred times the benefits of the two least effective methods, which means that for every $100 spent on one of the least effective methods, $99.50 is wasted. When resources are limited and education is so important to the future of children, that waste means that many human beings do not achieve their full potential.

Sometimes what’s exceptional about a sign is not the sign itself, but its timing. Your favorite pick-me-up song plays on the radio just when you’re feeling especially down. The number 100 appears on your Starbucks receipt just when you’re worrying about flunking a test. The answer to a crossword puzzle clue is randomly spoken by someone on TV just when you’re about to give up on it. All of these simple, surprising occurrences can be signs from the Other Side, because their timing makes us feel connected to the world in a way we can’t quite explain—as if all we have to do is release our feelings of fear and doubt into the universe, and the universe will respond with playful, wonderful reassurances.

If there are 60,000 blue marbles and 40,000 red marbles in a giant urn, then the most likely composition of a sample of 100 marbles drawn randomly from the urn would be 60 blue marbles and 40 red marbles.

Sending a man to the moon and finding Osama Bin Laden cost America about the same amount of time and money, 10 years and $100 billion.

RANDOM LIST OF REDUDENT SHIT Expecting brickwalls to transform into doors.... Expecting the blind to see beauty with eyesight.......Expecting an illiterate poet to write novels.......Expecting the electrician to fix the plumbing......Expecting comfort from the cause...... ...................................... Expecting the truth after a lie ...... Expecting love from the heartless.... Expecting a kingdom from a coward..... Expecting peace in the middle of war...... Expecting reciprocity from the greedy......Expecting a size 6 shoe to fit a size 9 foot....... Expecting 100% with only 14.28% of valenteered access.....Expecting your to be mine when you don't want me as yours.

I believe the technology industry is going in a different direction in which one may think about the Metaverse. Why spend trillions of dollars on big data when big data is becoming more useless? We need dynamic content to create a boom in the tech industry for the next millennium. Why hire someone with a 4 year degree in college for a career in database administration when companies can't afford to pay 100k a year? We can manage that quite fine in Google sheets or excel. The creation of AI would then completely defeat the purpose of data as a service when a program can dynamically build meta searcheable objects in random access memory and store them Inna virtualized file container ;)." - Jonathan Roy Mckinney

I believe the technological industry is switching in a different direction that one may think in the Metaverse. Why spend trillions of dollars on big data when it is becoming more useless? We need dynamic content to create a boom in the tech industry for the next millennium. Why hire someone with a 4 year degree from college for a career in database administration when companies can't afford to pay 100k a year? We can manage it quite fine in google sheets or excel. The utilization of AI will then completely defeat the purpose of Data As A Service when a program can dynamically build hash objects in random access memory by simply using a small script like (via switch) while creating a [5th XYZ Stargate] just like the Diablo version, but with a smaller seed. You could then store those objects for the blockchain Inna virtualized file container ;)." - Jonathan Roy Mckinney

I define that the tech industry switches in all directions contrary to what people believe as the norm for the new Metaverse. Why spend trillions of dollars on big data when it is becoming more useless? We need dynamic content to create a boom in the tech industry for the next millennium. Why hire someone with a 4 year college degree for a career in database administration when companies can't afford to pay 100k a year? We can manage information stores perfectly fine with google sheets or microsoft excel. I thought that utilizing AI would completely switch off problematics in relationship to Data As A Service when programs are dynamically building hash tables for objects in random access memory, storing them as blockchains Inna virtualized file container ;)." - Jonathan Roy Mckinney

The thirty-day no-contact rule Recovering from a breakup on a more practical basis can be likened to getting over an addiction. You go through periods of major withdrawal where you become overwhelmed by a cocktail of emotions, including guilt, fear, randomly missing him, and suddenly feeling like what he did to you ‘wasn’t that bad’. You start to play the mental showreel of all your good times (even if you only had a few), and suddenly you can’t remember why you left. Feeling this cluster of imbalanced emotions can be very confusing and irritating, but all hope is not lost. Contrary to popular belief, breakups don’t actually have to be hard. We assign so much spiritual and emotional value to these men, that by the time we finally distance ourselves from them, we feel distant from ourselves. And that’s really heartbreaking, because no man is worth losing yourself over. Ever. They say it takes about thirty days to break a habit. Texting your ex, stalking his profile from your second account, deliberately asking your mutual friends certain questions to get updates on his life and his new girl – it all needs to stop. So right now, go cold turkey, block his number on whatever messaging app you use, remove him from all your social media. Maintaining little corridors of access to him means he’s still on a pedestal. It also means your value system when it comes to men is warped, because naturally you’re going to keep comparing new guys to him as long as he holds this much space in your head. You want to evict him from that space so that someone new can blow you away when the time is right! This guy is not the be-all and end-all of your experiences with men, and the outcome of your situation with him really doesn’t have to define your future relationships. This thirty-day period of making yourself the centre of your world has a 100 per cent success rate, because by the time you get to day thirty, if it’s done honestly and correctly, you will have either a) met a new guy or b) found a whole heap of new reasons to love your healing self. But the thirty-day no-contact rule must be adhered to strictly, and if you break the pact with yourself, you must start all the way from the beginning – which might feel like torture.

The Lone Star State has sued the United States more than 40 times in the past 100 years over everything from women’s health to the environment. Most of those lawsuits occurred during the Obama Administration.

Neither Duncan nor I could see how to solve that problem by pure mathematics, so we used a computer to simulate the morph on networks of large but manageable size, starting from pristine rings with 1,000 nodes and 10 links per node. To chart the structural changes in the middle ground, we graphed both the average path length and the clustering as functions of the proportion of links that were randomly rewired. What we found amazed us. The slightest bit of randomness contracted the network tremendously. The average path length plummeted at first—with only 1 percent rewiring (meaning that only 1 out of every 100 links was randomized), the graph dropped by 85 percent from its original level. Further rewiring had only a minimal effect; the curve leveled off onto a low-lying plateau, indicating that the network had already gotten about as small as it could possibly get, as if it were completely random. Meanwhile, the clustering barely budged. With 1 percent rewiring, the clustering dropped by only 3 percent. Connections were being yanked out of well-ordered neighborhoods, yet the clustering hardly noticed. Only much later in the morph, long after the crash in path length, did clustering begin to drop significantly.

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observation is simply an observation for which a specified outcome has not yet occurred. Assume that data exist from a random sample of 100 clients who are seeking, or have found, employment. Survival analysis is the statistical procedure for analyzing these data. The name of this procedure stems from its use in medical research. In clinical trials, researchers want to know the survival (or disease) rate of patients as a function of the duration of their treatment. For patients in the middle of their trial, the specified outcome may not have occurred yet. We obtain the following results (also called a life table) from analyzing hypothetical data from welfare records (see Table 18.3). In the context shown in the table, the word terminal signifies that the event has occurred. That is, the client has found employment. At start time zero, 100 cases enter the interval. During the first period, there are no terminal cases and nine censored cases. Thus, 91 cases enter the next period. In this second period, 2 clients find employment and 14 do not, resulting in 75 cases that enter the following period. The column labeled “Cumulative proportion surviving until end of interval” is an estimate of probability of surviving (not finding employment) until the end of the stated interval.5 The column labeled “Probability density” is an estimate of the probability of the terminal event occurring (that is, finding employment) during the time interval. The results also report that “the median survival time is 5.19.” That is, half of the clients find employment in 5.19 weeks. Table 18.2 Censored Observations Note: Obs = observations (clients); Emp = employment; 0 = has not yet found employment; 1 = has found employment. Table 18.3 Life Table Results

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It may seem unlikely in principle that one individual could really generate so much more wealth than another. The key to this mystery is to revisit that question, are they really worth 100 of us? Would a basketball team trade one of their players for 100 random people? What would Apple’s next product look like if you replaced Steve Jobs with a committee of 100 random people?6 These things don’t scale linearly. Perhaps the CEO or the professional athlete has only ten times (whatever that means) the skill and determination of an ordinary person. But it makes all the difference that it’s concentrated in one individual.

Warner Brothers received more than 100 death threats for pushing the release date of Half-Blood Prince to 2009 when it was scheduled to be released in fall 2008.

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