Data Standardization Quotes

Women have always worked. They have worked unpaid, underpaid, underappreciated, and invisibly, but they have always worked. But the modern workplace does not work for women. From its location, to its hours, to its regulatory standards, it has been designed around the lives of men and it is no longer fit for purpose. The world of work needs a wholesale redesign--of its regulations, of its equipment, of its culture--and this redesign must be led by data on female bodies and female lives. We have to start recognising that the work women do is not an added extra, a bonus that we could do without: women's work, paid and unpaid, is the backbone of our society and our economy. It's about time we started valuing it.

The five-star scale doesn’t really exist for humans; it exists for data aggregation systems, which is why it did not become standard until the internet era. Making conclusions about a book’s quality from a 175-word review is hard work for artificial intelligences, whereas star ratings are ideal for them.

Women tend to sit further forward than men when driving. This is because we are on average shorter. Our legs need to be closer to reach the pedals, and we need to sit more upright to see clearly over the dashboard.49 This is not, however, the ‘standard seating position’. Women are ‘out of position’ drivers.50 And our wilful deviation from the norm means that we are at greater risk of internal injury on frontal collisions.51

I find it hard to talk about myself. I'm always tripped up by the eternal who am I? paradox. Sure, no one knows as much pure data about me as me. But when I talk about myself, all sorts of other factors--values, standards, my own limitations as an observer--make me, the narrator, select and eliminate things about me, the narratee. I've always been disturbed by the thought that I'm not painting a very objective picture of myself. This kind of thing doesn't seem to bother most people. Given the chance, people are surprisingly frank when they talk about themselves. "I'm honest and open to a ridiculous degree," they'll say, or "I'm thin-skinned and not the type who gets along easily in the world." Or "I am very good at sensing others' true feelings." But any number of times I've seen people who say they've easily hurt other people for no apparent reason. Self-styled honest and open people, without realizing what they're doing, blithely use some self-serving excuse to get what they want. And those "good at sensing others' true feelings" are duped by the most transparent flattery. It's enough to make me ask the question: How well do we really know ourselves? The more I think about it, the more I'd like to take a rain check on the topic of me. What I'd like to know more about is the objective reality of things outside myself. How important the world outside is to me, how I maintain a sense of equilibrium by coming to terms with it. That's how I'd grasp a clearer sense of who I am.

We must surrender our skepticism only in the face of rock-solid evidence. Science demands a tolerance for ambiguity. Where we are ignorant, we withhold belief. Whatever annoyance the uncertainty engenders serves a higher purpose: It drives us to accumulate better data. This attitude is the difference between science and so much else. Science offers little in the way of cheap thrills. The standards of evidence are strict. But when followed they allow us to see far, illuminating even a great darkness.

Serving humanity intelligently is held up as the “gold standard” of AI based systems. But, with the emergence of new technologies and AI systems with bio-metric data storage, surveillance, tracking and big data analysis, humanity and the society is facing a threat today from evilly designed AI systems in the hands of monster governments and irresponsible people. Humanity is on the verge of digital slavery.

The human victims of WMDs, we’ll see time and again, are held to a far higher standard of evidence than the algorithms themselves.

And so, because business leadership is still so dominated by men, modern workplaces are riddled with these kind of gaps, from doors that are too heavy for the average woman to open with ease, to glass stairs and lobby floors that mean anyone below can see up your skirt, to paving that’s exactly the right size to catch your heels. Small, niggling issues that aren’t the end of the world, granted, but that nevertheless irritate. Then there’s the standard office temperature. The formula to determine standard office temperature was developed in the 1960s around the metabolic resting rate of the average forty-year-old, 70 kg man.1 But a recent study found that ‘the metabolic rate of young adult females performing light office work is significantly lower’ than the standard values for men doing the same type of activity. In fact, the formula may overestimate female metabolic rate by as much as 35%, meaning that current offices are on average five degrees too cold for women. Which leads to the odd sight of female office workers wrapped up in blankets in the New York summer while their male colleagues wander around in summer clothes.

The average female handspan is between seven and eight inches,2 which makes the standard forty-eight-inch keyboard something of a challenge. Octaves on a standard keyboard are 7.4 inches wide, and one study found that this keyboard disadvantages 87% of adult female pianists.3 Meanwhile, a 2015 study which compared the handspan of 473 adult pianists to their ‘level of acclaim’ found that all twelve of the pianists considered to be of international renown had spans of 8.8 inches or above.

He’s already run the standard battery of questions, checked the check boxes, computed the data: hears voices = schizophrenic; too agitated = paranoid; too bright = manic; too moody = bipolar; and of course everyone knows a depressive, a suicidal, and if you’re all-around too unruly or obstructive or treatment resistant like a superbug, you get slapped with a personality disorder, too. In Crote Six, they said I “suffer” from schizoaffective disorder. That’s like the sampler plate of diagnoses, Best of Everything. But I don’t want to suffer. I want to live.

During the dot-com bubble, most people did not use a persuasive theory to gauge whether stock prices were too high, too low, or just right. Instead, as they watched stock prices go up, they invented explanations to rationalize what was happening. They talked about Moore’s Law, smart kids, and Alan Greenspan. Data without theory.

The value for which P=0.05, or 1 in 20, is 1.96 or nearly 2; it is convenient to take this point as a limit in judging whether a deviation ought to be considered significant or not. Deviations exceeding twice the standard deviation are thus formally regarded as significant. Using this criterion we should be led to follow up a false indication only once in 22 trials, even if the statistics were the only guide available. Small effects will still escape notice if the data are insufficiently numerous to bring them out, but no lowering of the standard of significance would meet this difficulty.

Leyner's fiction is, in this regard, an eloquent reply to Gilder's prediction that our TV-culture problems can be resolved by the dismantling of images into discrete chunks we can recombine as we fancy. Leyner's world is a Gilder-esque dystopia. The passivity and schizoid decay still endure for Leyner in his characters' reception of images and waves of data. The ability to combine them only adds a layer of disorientation: when all experience can be deconstructed and reconfigured, there become simply too many choices. And in the absence of any credible, noncommercial guides for living, the freedom to choose is about as "liberating" as a bad acid trip: each quantum is as good as the next, and the only standard of an assembly's quality is its weirdness, incongruity, its ability to stand out from a crowd of other image-constructs and wow some Audience.

At the time, when decisions had to be made, she had truly wanted to stay home- she was, in a word, exchausted- though she had never wanted such a thing before. And, honestly, what a privilege. What a treat. She understood that she was just a privileged, overeducated lady in the middle of America living the dream of holding her baby twenty-four hours a day. According to basically everyone’s standards, she had nothing to complain about, ever, after that point and possibly even leading up to it. In fact, wasn’t it a bit, you know, hoity-toity, a but oblivious middle-class white lady of her, even to think about complaining? If she read the articles, examined the data, contemplated her lot in life, her place in society, her historical role in the oppression of everyone other than white men, she really had not even a sparse spot of yard on which to stand and emit one single strangled scream.

The five-star scale doesn’t really exist for humans; it exists for data aggregation systems, which is why it did not become standard until the internet era.

While those p-values have been the standard for decades, they were arbitrarily chosen, leading some modern data scientists to question their usefulness.

ARPA should not force the research computers at each site to handle the routing of data, Clark argued. Instead ARPA should design and give each site a standardized minicomputer that would do the routing.

Fortunately, our colleges and universities are fully cognizant of the problems I have been delineating and take concerted action to address them. Curricula are designed to give coherence to the educational experience and to challenge students to develop a strong degree of moral awareness. Professors, deeply involved with the enterprise of undergraduate instruction, are committed to their students' intellectual growth and insist on maintaining the highest standards of academic rigor. Career services keep themselves informed about the broad range of postgraduate options and make a point of steering students away from conventional choices. A policy of noncooperation with U.S. News has taken hold, depriving the magazine of the data requisite to calculate its rankings. Rather than squandering money on luxurious amenities and exorbitant administrative salaries, schools have rededicated themselves to their core missions of teaching and the liberal arts. I'm kidding, of course.

The first is that brains, by contrast to the kinds of program we typically run on our computers, do not use standardized data storage and representation formats. Rather, each brain develops its own idiosyncratic representations of higher-level content.

In the majority of schools, what's needed isn't more professional development on deconstructing standards or academic discourse or using data to drive instruction. What's needed is time, space, and attention to managing stress and cultivating resilience.

Amidst all this organic plasticity and compromise, though, the infrastructure fields could still stake out territory for a few standardized subsystems, identical from citizen to citizen. Two of these were channels for incoming data—one for gestalt, and one for linear, the two primary modalities of all Konishi citizens, distant descendants of vision and hearing. By the orphan's two-hundredth iteration, the channels themselves were fully formed, but the inner structures to which they fed their data, the networks for classifying and making sense of it, were still undeveloped, still unrehearsed. Konishi polis itself was buried two hundred meters beneath the Siberian tundra, but via fiber and satellite links the input channels could bring in data from any forum in the Coalition of Polises, from probes orbiting every planet and moon in the solar system, from drones wandering the forests and oceans of Earth, from ten million kinds of scape or abstract sensorium. The first problem of perception was learning how to choose from this superabundance.

Look, cell phone geolocation data shows very few clustering anomalies for this hour and climate. And that’s holding up pretty much across all major metro areas. It’s gone down six percentage points since news of the Karachi workshop hit the Web, and it’s trending downward. If people are protesting, they aren’t doing it in the streets.” He circled his finger over a few clusters of dots. “Some potential protest knots in Portland and Austin, but defiance-related tag cloud groupings in social media put us within the three-sigma rule—meaning roughly sixty-eight percent of the values lie within one standard deviation of the mean.

Avoid succumbing to the gambler’s fallacy or the base rate fallacy. Anecdotal evidence and correlations you see in data are good hypothesis generators, but correlation does not imply causation—you still need to rely on well-designed experiments to draw strong conclusions. Look for tried-and-true experimental designs, such as randomized controlled experiments or A/B testing, that show statistical significance. The normal distribution is particularly useful in experimental analysis due to the central limit theorem. Recall that in a normal distribution, about 68 percent of values fall within one standard deviation, and 95 percent within two. Any isolated experiment can result in a false positive or a false negative and can also be biased by myriad factors, most commonly selection bias, response bias, and survivorship bias. Replication increases confidence in results, so start by looking for a systematic review and/or meta-analysis when researching an area.

Developing and maintaining integrity require constant attention. John Weston, chairman and CEO of Automatic Data Processing, Inc., says, “I`ve always tried to live with the following simple rule: Don`t do what you wouldn`t feel comfortable reading about in the newspapers the next day.” That`s a good standard all of us should keep.

In emerging technologies, security is the biggest threat, and common standards for communication and safety are improving, which means that risks will be minimised. We can only hope that man with this technology can actually stop the destruction of our planet, make the population healthier, and create a better future for all of us.

perfectionism is a desperate attempt to live up to impossible standards. Perfectionism will do anything to protect those impossible standards. It can’t let you find out how impossible they are, especially with the cold eye of data, so it terrifies you into thinking that you’ll be crushed by disappointment if you peer behind that curtain. Data would tell you that your bank account is low, but you’re spending a lot more on coffee than you think. If you started making it at home, you could easily start saving for a vacation. You might even stop comparing yourself to the impossible financial standards of your friends online. You might make some reasonable goals and completely change the way you view money. You might even have fun.

facsimile science. (By this term I mean materials that carry the accoutrements of science—including in some cases peer review—but fail to adhere to accepted scientific standards such as methodological naturalism, complete and open reporting of data, and the willingness to revise assumptions in the light of data.)49 This is the problem of for-profit and predatory conferences and journals.

Our inherited desire to explain what we see fuels two kinds of cognitive errors. First, we are too easily seduced by patterns and by the theories that explain them. Second, we latch onto data that support our theories and discount contradicting evidence. We believe stories simply because they are consistent with the patterns we observe and, once we have a story, we are reluctant to let it go.

Even though bitcoin may not, after all, represent the potential for a new gold standard, its underlying technology will unbundle the roles of money. This can finally clarify and enable the necessary distinction between the medium of exchange and the measuring stick. Disaggregated will be all the GAFAM (Google, Apple, Facebook, Amazon, Microsoft conglomerates)—the clouds of concentrated computing and commerce.

But the story of BPA is not just about gender: it’s also about class. Or at least it’s about gendered class. Fearing a major consumer boycott, most baby-bottle manufacturers voluntarily removed BPA from their products, and while the official US line on BPA is that it is not toxic, the EU and Canada are on their way to banning its use altogether. But the legislation that we have exclusively concerns consumers: no regulatory standard has ever been set for workplace exposure.5 ‘It was ironic to me,’ says occupational health researcher Jim Brophy, ‘that all this talk about the danger for pregnant women and women who had just given birth never extended to the women who were producing these bottles. Those women whose exposures far exceeded anything that you would have in the general environment. There was no talk about the pregnant worker who is on the machine that’s producing this thing.

The master propagandist, like the advertising expert, avoids obvious emotional appeals and strives for a tone that is consistent with the prosaic quality of modern life—a dry, bland matter-of-factness. Nor does the propagandist circulate "intentionally biased" information. He knows that partial truths serve as more effective instruments of deception than lies. Thus he tries to impress the public with statistics of economic growth that neglect to give the base year from which growth is calculated, with accurate but meaningless facts about the standard of living—with raw and uninterpreted data, in other words, from which the audience is invited to draw the inescapable conclusion that things are getting better and the present régime therefore deserves the people's confidence, or on the other hand that things are getting worse so rapidly that the present régime should be given emergency powers to deal with the developing crisis.

Twenty minutes later, I was sitting in the federal building that housed the Department of Homeland Security, about fifteen stories up, locked in a standard federal issue interrogation room. Metal chair, metal table, big one-way mirror window, just like the movies. My arms were bound behind me with at least three flex-cuffs. The only addition to the room were the four tactical team members standing in each corner of the room, M4 rifles slung across their chests. Books, Splitter, Data and old Rattler himself, Agent Simmons.

I find it hard to talk about myself. I’m always tripped up by the eternal who am I? paradox. Sure, no one knows as much pure data about me as me. But when I talk about myself, all sorts of other factors – values, standards, my own limitations as an observer – make me, the narrator, select and eliminate things about me, the narratee. I’ve always been disturbed by the thought that I’m not painting a very objective picture of myself. This kind of thing doesn’t seem to bother most people. Given the chance, they’re surprisingly frank when they talk about themselves. “I’m honest and open to a ridiculous degree,” they’ll say, or “I’m thin-skinned and not the type who gets along easily in the world,” or “I’m very good at sensing others’ true feelings.” But any number of times I’ve seen people who say they’re easily hurt or hurt other people for no apparent reason. Self-styled honest and open people, without realizing what they’re doing, blithely use some self-serving excuse to get what they want. And those who are “good at sensing others’ true feelings” are taken in by the most transparent flattery. It’s enough to make me ask the question: how well do we really know ourselves?

The vast majority of scientists deserve our trust. But no matter how you slice it, scientific fraud isn’t rare. Hundreds of scientific papers get retracted every year, and while firm numbers are elusive, something like half of them are retracted due to fraud or other misconduct. Even big-name scientists transgress. Again, it’s unfair to condemn people from the past for failing to meet today’s standards, but historians have noted that Galileo, Newton, Bernoulli, Dalton, Mendel, and more all manipulated experiments and/or fudged data in ways that would have gotten them fired from any self-respecting lab today.

A scientist must put faith in the experimental data reported by other scientists, and in the institutions that sponsored those scientists, and in the standards by which those scientists received their credentials. A scientist must put faith in the authority of the journals that publish the results of various studies. Finally, but perhaps most fundamentally, a scientist must trust that empirical reality is indeed perceptible and measurable, and that the laws of cause and effect will apply universally. No scientific endeavor can proceed if the experimenter subjects every phenomenon to radical doubt, disqualifying his own observations as well as those of his peers. Polanyi concluded that science proceeds from a trust that is “fiduciary”—a word that derives from the Latin root meaning “faith-based.” Such faith is well placed and well founded, and it enables science to proceed apace; but, nonetheless, it is a species of faith, not an absolutely certain knowledge. “We must now recognize belief once more as the source of all knowledge,…” Polanyi said. “No intelligence, however critical or original, can operate outside such a fiduciary framework.” Secularism’s attempts to replace the authority of religion with a supposed “authority of experience and reason” has proven, in Polanyi’s words, “farcically inadequate

The formula to determine standard office temperature was developed in the 1960s around the metabolic resting rate of the average forty-year-old, 70 kg man.1 But a recent study found that ‘the metabolic rate of young adult females performing light office work is significantly lower’ than the standard values for men doing the same type of activity. In fact, the formula may overestimate female metabolic rate by as much as 35%, meaning that current offices are on average five degrees too cold for women. Which leads to the odd sight of female office workers wrapped up in blankets in the New York summer while their male colleagues wander around in summer clothes.

Every year or so I like to take a step back and look at a few key advertising, marketing, and media facts just to gauge how far removed from reality we advertising experts have gotten. These data represent the latest numbers I could find. I have listed the sources below. So here we go -- 10 facts, direct from the real world: E-commerce in 2014 accounted for 6.5 percent of total retail sales. 96% of video viewing is currently done on a television. 4% is done on a web device. In Europe and the US, people would not care if 92% of brands disappeared. The rate of engagement among a brand's fans with a Facebook post is 7 in 10,000. For Twitter it is 3 in 10,000. Fewer than one standard banner ad in a thousand is clicked on. Over half the display ads paid for by marketers are unviewable. Less than 1% of retail buying is done on a mobile device. Only 44% of traffic on the web is human. One bot-net can generate 1 billion fraudulent digital ad impressions a day. Half of all U.S online advertising - $10 billion a year - may be lost to fraud. As regular readers know, one of our favorite sayings around The Ad Contrarian Social Club is a quote from Noble Prize winning physicist Richard Feynman, who wonderfully declared that “Science is the belief in the ignorance of experts.” I think these facts do a pretty good job of vindicating Feynman.

Then there’s the standard office temperature. The formula to determine standard office temperature was developed in the 1960s around the metabolic resting rate of the average forty-year-old, 70 kg man. 1 But a recent study found that ‘the metabolic rate of young adult females performing light office work is significantly lower’ than the standard values for men doing the same type of activity. In fact, the formula may overestimate female metabolic rate by as much as 35%, meaning that current offices are on average five degrees too cold for women. Which leads to the odd sight of female office workers wrapped up in blankets in the New York summer while their male colleagues wander around in summer clothes.

It’s easy to raise graduation rates, for example, by lowering standards. Many students struggle with math and science prerequisites and foreign languages. Water down those requirements, and more students will graduate. But if one goal of our educational system is to produce more scientists and technologists for a global economy, how smart is that? It would also be a cinch to pump up the income numbers for graduates. All colleges would have to do is shrink their liberal arts programs, and get rid of education departments and social work departments while they’re at it, since teachers and social workers make less money than engineers, chemists, and computer scientists. But they’re no less valuable to society.

It has often been claimed that there has been very little change in the average real income of American households over a period of decades. It is an undisputed fact that the average real income—that is, money income adjusted for inflation—of American households rose by only 6 percent over the entire period from 1969 to 1996. That might well be considered to qualify as stagnation. But it is an equally undisputed fact that the average real income per person in the United States rose by 51 percent over that very same period.3 How can both these statistics be true? Because the average number of individuals per household has been declining over the years. Half the households in the United States contained six or more people in 1900, as did 21 percent in 1950. But, by 1998, only ten percent of American households had that many people.4 The average number of persons per household not only varies over time, it also varies from one racial or ethnic group to another at a given time, and varies from one income bracket to another. As of 2007, for example, black household income was lower than Hispanic household income, even though black per capita income was higher than Hispanic per capita income, because black households average fewer people than Hispanic households. Similarly, Asian American household income was higher than white household income, even though white per capita income was higher than Asian American per capita income, because Asian American households average more people.5 Income comparisons using household statistics are far less reliable indicators of standards of living than are individual income data because households vary in size while an individual always means one person. Studies of what people actually consume—that is, their standard of living—show substantial increases over the years, even among the poor,6 which is more in keeping with a 51 percent increase in real per capita income than with a 6 percent increase in real household income. But household income statistics present golden opportunities for fallacies to flourish, and those opportunities have been seized by many in the media, in politics, and in academia.

I find it hard to talk about myself. I'm always tripped up by the eternal who am I? paradox. Sure, no one knows as much pure data about me as me. But when I talk about myself, all sorts of other factors - values, standards, my own limitations as an observer - make me, the narrator, select and eliminate things about me, the narratee. I've always been disturbed by the thought that I'm not painting a very objective picture of myself. This kind of things doesn't seem to bother most people. Given the chance, people are surprisingly frank when they talk about themselves. "I'm honest and open to a ridiculous degree," they'll say, or "I'm thin-skinned and not the type who gets along easily in the world." Or "I'm very good at sensing others' true feelings." But any number of times I've seen people who say they're easily hurt or hurt other people for no apparent reason. Self-styled honest and open people, without realizing what they're doing, blithely use some self-serving excuse to get what they want. And those "good at sensing others' true feelings" are taken in by the most transparent flattery. It's enough to make me ask the question: how well do really know ourselves? The more I think about it, the more I'd like to take a rain check on the topic of me. What I'd like to know more about is the objective reality of things outside myself. How important the world outside is to me, how I maintain a sense of equilibrium by coming to terms with it. That's how I'd grasp a clearer sense of who I am. These are the kind of ideas I had running through my head when I was a teenager. Like a master builder stretches taut his string and lays one brick after another, I constructed this viewpoint - or philosophy of life, to put a bigger spin on it. Logic and speculation played a part in formulating this viewpoint, but for the most part it was based on my own experiences. And speaking of experience, a number of painful episodes taught me that getting this viewpoint of mine across to other people wasn't the easiest thing in the world. The upshot of all this is that when I was young I began to draw an invisible boundary between myself and other people. No matter who I was dealing with, I maintained a set distance, carefully monitoring the person's attitude so that they wouldn't get any closer. I didn't easily swallow what other people told me. My only passions were books and music. As you might guess, I led a lonely life.

Quite often, the Tesla engineers brought their Silicon Valley attitude to the automakers’ traditional stomping grounds. There’s a break and traction testing track in northern Sweden near the Arctic Circle where cars get tuned on large plains of ice. It would be standard to run the car for three days or so, get the data, and return to company headquarters for many weeks of meetings about how to adjust the car. The whole process of tuning a car can take the entire winter. Tesla, by contrast, sent its engineers along with the Roadsters being tested and had them analyze the data on the spot. When something needed to be tweaked, the engineers would rewrite some code and send the car back on the ice. “BMW would need to have a confab between three or four companies that would all blame each other for the problem,” Tarpenning said. “We just fixed it ourselves.

G. Stanley Hall, a creature of his times, believed strongly that adolescence was determined – a fixed feature of human development that could be explained and accounted for in scientific fashion. To make his case, he relied on Haeckel's faulty recapitulation idea, Lombroso's faulty phrenology-inspired theories of crime, a plethora of anecdotes and one-sided interpretations of data. Given the issues, theories, standards and data-handling methods of his day, he did a superb job. But when you take away the shoddy theories, put the anecdotes in their place, and look for alternate explanations of the data, the bronze statue tumbles hard. I have no doubt that many of the street teens of Hall's time were suffering or insufferable, but it's a serious mistake to develop a timeless, universal theory of human nature around the peculiarities of the people of one's own time and place.

von Braun went looking for problems, hunches, and bad news. He even rewarded those who exposed problems. After Kranz and von Braun’s time, the “All Others Bring Data” process culture remained, but the informal culture and power of individual hunches shriveled. In 1974, William Lucas took over the Marshall Space Flight Center. A NASA chief historian wrote that Lucas was a brilliant engineer but “often grew angry when he learned of problems.” Allan McDonald described him to me as a “shoot-the-messenger type guy.” Lucas transformed von Braun’s Monday Notes into a system purely for upward communication. He did not write feedback and the notes did not circulate. At one point they morphed into standardized forms that had to be filled out. Monday Notes became one more rigid formality in a process culture. “Immediately, the quality of the notes fell,” wrote another official NASA historian.

Recent studies indicate that boys raised by women, including single women and lesbian couples, do not suffer in their adjustment; they are not appreciably less “masculine”; they do not show signs of psychological impairment. What many boys without fathers inarguably do face is a precipitous drop in their socioeconomic status. When families dissolve, the average standard of living for mothers and children can fall as much as 60 percent, while that of the man usually rises. When we focus on the highly speculative psychological effects of fatherlessness we draw away from concrete political concerns, like the role of increased poverty. Again, there are as yet no data suggesting that boys without fathers to model masculinity are necessarily impaired. Those boys who do have fathers are happiest and most well adjusted with warm, loving fathers, fathers who score high in precisely “feminine” qualities.

What are the health effects of the choice between austerity and stimulus? Today there is a vast natural experiment being conducted on the body economic. It is similar to the policy experiments that occurred in the Great Depression, the post-communist crisis in eastern Europe, and the East Asian Financial Crisis. As in those prior trials, health statistics from the Great Recession reveal the deadly price of austerity—a price that can be calculated not just in the ticks to economic growth rates, but in the number of years of life lost and avoidable deaths. Had the austerity experiments been governed by the same rigorous standards as clinical trials, they would have been discontinued long ago by a board of medical ethics. The side effects of the austerity treatment have been severe and often deadly. The benefits of the treatment have failed to materialize. Instead of austerity, we should enact evidence-based policies to protect health during hard times. Social protection saves lives. If administered correctly, these programs don’t bust the budget, but—as we have shown throughout this book—they boost economic growth and improve public health. Austerity’s advocates have ignored evidence of the health and economic consequences of their recommendations. They ignore it even though—as with the International Monetary Fund—the evidence often comes from their own data. Austerity’s proponents, such as British Prime Minister David Cameron, continue to write prescriptions of austerity for the body economic, in spite of evidence that it has failed. Ultimately austerity has failed because it is unsupported by sound logic or data. It is an economic ideology. It stems from the belief that small government and free markets are always better than state intervention. It is a socially constructed myth—a convenient belief among politicians taken advantage of by those who have a vested interest in shrinking the role of the state, in privatizing social welfare systems for personal gain. It does great harm—punishing the most vulnerable, rather than those who caused this recession.

The National Institute of Standards and Technology has provided a preliminary estimation that between 16,400 and 18,800 civilians were in the WTC complex as of 8:46 am on September 11. At most 2,152 individual died in the WTC complex who were not 1) fire or police first responders, 2) security or fire safety personnel of the WTC or individual companies, 3) volunteer civilians who ran to the WTC after the planes' impact to help others or, 4) on the two planes that crashed into the Twin Towers. Out of this total number of fatalities, we can account for the workplace location of 2,052 individuals, or 95.35 percent. Of this number, 1,942 or 94.64 percent either worked or were supposed to attend a meeting at or above the respective impact zones of the Twin Towers; only 110, or 5.36 percent of those who died, worked below the impact zone. While a given person's office location at the WTC does not definitively indicate where that individual died that morning or whether he or she could have evacuated, these data strongly suggest that the evacuation was a success for civilians below the impact zone.

There was little effort to conceal this method of doing business. It was common knowledge, from senior managers and heads of research and development to the people responsible for formulation and the clinical people. Essentially, Ranbaxy’s manufacturing standards boiled down to whatever the company could get away with. As Thakur knew from his years of training, a well-made drug is not one that passes its final test. Its quality must be assessed at each step of production and lies in all the data that accompanies it. Each of those test results, recorded along the way, helps to create an essential roadmap of quality. But because Ranbaxy was fixated on results, regulations and requirements were viewed with indifference. Good manufacturing practices were stop signs and inconvenient detours. So Ranbaxy was driving any way it chose to arrive at favorable results, then moving around road signs, rearranging traffic lights, and adjusting mileage after the fact. As the company’s head of analytical research would later tell an auditor: “It is not in Indian culture to record the data while we conduct our experiments.

I find it hard to talk about myself. I’m always tripped up by the eternal who am I? paradox. Sure, no one knows as much pure data about me as me. But when I talk about myself, all sorts of other factors—values, standards, my own limitations as an observer—make me, the narrator, select and eliminate things about me, the narratee. I’ve always been disturbed by the thought that I’m not painting a very objective picture of myself. This kind of thing doesn’t seem to bother most people. Given the chance, people are surprisingly frank when they talk about themselves. “I’m honest and open to a ridiculous degree,” they’ll say, or “I’m thin-skinned and not the type who gets along easily in the world.” Or “I am very good at sensing others’ true feelings.” But any number of times I’ve seen people who say they’re easily hurt hurt other people for no apparent reason. Self-styled honest and open people, without realizing what they’re doing, blithely use some self-serving excuse to get what they want. And those “good at sensing others’ true feelings” are duped by the most transparent flattery. It’s enough to make me ask the question: How well do we really know ourselves?

The human mind wants to “win” whatever game is being played. This pitfall is evident in many areas of life. We focus on working long hours instead of getting meaningful work done. We care more about getting ten thousand steps than we do about being healthy. We teach for standardized tests instead of emphasizing learning, curiosity, and critical thinking. In short, we optimize for what we measure. When we choose the wrong measurement, we get the wrong behavior. This is sometimes referred to as Goodhart’s Law. Named after the economist Charles Goodhart, the principle states, “When a measure becomes a target, it ceases to be a good measure.”9 Measurement is only useful when it guides you and adds context to a larger picture, not when it consumes you. Each number is simply one piece of feedback in the overall system. In our data-driven world, we tend to overvalue numbers and undervalue anything ephemeral, soft, and difficult to quantify. We mistakenly think the factors we can measure are the only factors that exist. But just because you can measure something doesn’t mean it’s the most important thing. And just because you can’t measure something doesn’t mean it’s not important at all.

When scientific proposals are brought forward, they are not judged by hunches or gut feelings. Only one standard is relevant: a proposal's ability to explain or predict experimental data and astronomical observations. Therein lies the singular beauty of science. As we struggle toward deeper understanding, we must give our creative imagination ample room to explore. We must be willing to step outside conventional ideas and established frameworks. But unlike the wealth of other human activities through which the creative impulse is channeled, science supplies a final reckoning, a built-in assessment of what's right and what's not. A complication of scientific life in the late twentieth and early twenty-first centuries is that some of our theoretical ideas have soared past our ability to test or observe. String theory has for some time been the poster child for this situation; the possibility that we're part of a multiverse provides an even more sprawling example. I've laid out a general prescription for how a multiverse proposal might be testable, but at our current level of understanding none of the multiverse theories we've encountered yet meet the criteria. With ongoing research, this situation could greatly improve.

The best entrepreneurs don’t just follow Moore’s Law; they anticipate it. Consider Reed Hastings, the cofounder and CEO of Netflix. When he started Netflix, his long-term vision was to provide television on demand, delivered via the Internet. But back in 1997, the technology simply wasn’t ready for his vision—remember, this was during the era of dial-up Internet access. One hour of high-definition video requires transmitting 40 GB of compressed data (over 400 GB without compression). A standard 28.8K modem from that era would have taken over four months to transmit a single episode of Stranger Things. However, there was a technological innovation that would allow Netflix to get partway to Hastings’s ultimate vision—the DVD. Hastings realized that movie DVDs, then selling for around $ 20, were both compact and durable. This made them perfect for running a movie-rental-by-mail business. Hastings has said that he got the idea from a computer science class in which one of the assignments was to calculate the bandwidth of a station wagon full of backup tapes driving across the country! This was truly a case of technological innovation enabling business model innovation. Blockbuster Video had built a successful business around buying VHS tapes for around $ 100 and renting them out from physical stores, but the bulky, expensive, fragile tapes would never have supported a rental-by-mail business.

Pull approaches differ significantly from push approaches in terms of how they organize and manage resources. Push approaches are typified by "programs" - tightly scripted specifications of activities designed to be invoked by known parties in pre-determined contexts. Of course, we don't mean that all push approaches are software programs - we are using this as a broader metaphor to describe one way of organizing activities and resources. Think of thick process manuals in most enterprises or standardized curricula in most primary and secondary educational institutions, not to mention the programming of network television, and you will see that institutions heavily rely on programs of many types to deliver resources in pre-determined contexts. Pull approaches, in contrast, tend to be implemented on "platforms" designed to flexibly accommodate diverse providers and consumers of resources. These platforms are much more open-ended and designed to evolve based on the learning and changing needs of the participants. Once again, we do not mean to use platforms in the literal sense of a tangible foundation, but in a broader, metaphorical sense to describe frameworks for orchestrating a set of resources that can be configured quickly and easily to serve a broad range of needs. Think of Expedia's travel service or the emergency ward of a hospital and you will see the contrast with the hard-wired push programs.

Once again, he was deducing a theory from principles and postulates, not trying to explain the empirical data that experimental physicists studying cathode rays had begun to gather about the relation of mass to the velocity of particles. Coupling Maxwell’s theory with the relativity theory, he began (not surprisingly) with a thought experiment. He calculated the properties of two light pulses emitted in opposite directions by a body at rest. He then calculated the properties of these light pulses when observed from a moving frame of reference. From this he came up with equations regarding the relationship between speed and mass. The result was an elegant conclusion: mass and energy are different manifestations of the same thing. There is a fundamental interchangeability between the two. As he put it in his paper, “The mass of a body is a measure of its energy content.” The formula he used to describe this relationship was also strikingly simple: “If a body emits the energy L in the form of radiation, its mass decreases by L/V 2.” Or, to express the same equation in a different manner: L=mV 2. Einstein used the letter L to represent energy until 1912, when he crossed it out in a manuscript and replaced it with the more common E. He also used V to represent the velocity of light, before changing to the more common c. So, using the letters that soon became standard, Einstein had come up with his memorable equation: E=mc2

Modern statistics is built on the idea of models — probability models in particular. [...] The standard approach to any new problem is to identify the sources of variation, to describe those sources by probability distributions and then to use the model thus created to estimate, predict or test hypotheses about the undetermined parts of that model. […] A statistical model involves the identification of those elements of our problem which are subject to uncontrolled variation and a specification of that variation in terms of probability distributions. Therein lies the strength of the statistical approach and the source of many misunderstandings. Paradoxically, misunderstandings arise both from the lack of an adequate model and from over reliance on a model. […] At one level is the failure to recognise that there are many aspects of a model which cannot be tested empirically. At a higher level is the failure is to recognise that any model is, necessarily, an assumption in itself. The model is not the real world itself but a representation of that world as perceived by ourselves. This point is emphasised when, as may easily happen, two or more models make exactly the same predictions about the data. Even worse, two models may make predictions which are so close that no data we are ever likely to have can ever distinguish between them. […] All model-dependant inference is necessarily conditional on the model. This stricture needs, especially, to be borne in mind when using Bayesian methods. Such methods are totally model-dependent and thus all are vulnerable to this criticism. The problem can apparently be circumvented, of course, by embedding the model in a larger model in which any uncertainties are, themselves, expressed in probability distributions. However, in doing this we are embarking on a potentially infinite regress which quickly gets lost in a fog of uncertainty.

Though Hoover conceded that some might deem him a “fanatic,” he reacted with fury to any violations of the rules. In the spring of 1925, when White was still based in Houston, Hoover expressed outrage to him that several agents in the San Francisco field office were drinking liquor. He immediately fired these agents and ordered White—who, unlike his brother Doc and many of the other Cowboys, wasn’t much of a drinker—to inform all of his personnel that they would meet a similar fate if caught using intoxicants. He told White, “I believe that when a man becomes a part of the forces of this Bureau he must so conduct himself as to remove the slightest possibility of causing criticism or attack upon the Bureau.” The new policies, which were collected into a thick manual, the bible of Hoover’s bureau, went beyond codes of conduct. They dictated how agents gathered and processed information. In the past, agents had filed reports by phone or telegram, or by briefing a superior in person. As a result, critical information, including entire case files, was often lost. Before joining the Justice Department, Hoover had been a clerk at the Library of Congress—“ I’m sure he would be the Chief Librarian if he’d stayed with us,” a co-worker said—and Hoover had mastered how to classify reams of data using its Dewey decimal–like system. Hoover adopted a similar model, with its classifications and numbered subdivisions, to organize the bureau’s Central Files and General Indices. (Hoover’s “Personal File,” which included information that could be used to blackmail politicians, would be stored separately, in his secretary’s office.) Agents were now expected to standardize the way they filed their case reports, on single sheets of paper. This cut down not only on paperwork—another statistical measurement of efficiency—but also on the time it took for a prosecutor to assess whether a case should be pursued.

In April, Dr. Vladimir (Zev) Zelenko, M.D., an upstate New York physician and early HCQ adopter, reproduced Dr. Didier Raoult’s “startling successes” by dramatically reducing expected mortalities among 800 patients Zelenko treated with the HCQ cocktail.29 By late April of 2020, US doctors were widely prescribing HCQ to patients and family members, reporting outstanding results, and taking it themselves prophylactically. In May 2020, Dr. Harvey Risch, M.D., Ph.D. published the most comprehensive study, to date, on HCQ’s efficacy against COVID. Risch is Yale University’s super-eminent Professor of Epidemiology, an illustrious world authority on the analysis of aggregate clinical data. Dr. Risch concluded that evidence is unequivocal for early and safe use of the HCQ cocktail. Dr. Risch published his work—a meta-analysis reviewing five outpatient studies—in affiliation with the Johns Hopkins Bloomberg School of Public Health in the American Journal of Epidemiology, under the urgent title, “Early Outpatient Treatment of Symptomatic, High-Risk COVID-19 Patients that Should be Ramped-Up Immediately as Key to Pandemic Crisis.”30 He further demonstrated, with specificity, how HCQ’s critics—largely funded by Bill Gates and Dr. Tony Fauci31—had misinterpreted, misstated, and misreported negative results by employing faulty protocols, most of which showed HCQ efficacy administered without zinc and Zithromax which were known to be helpful. But their main trick for ensuring the protocols failed was to wait until late in the disease process before administering HCQ—when it is known to be ineffective. Dr. Risch noted that evidence against HCQ used late in the course of the disease is irrelevant. While acknowledging that Dr. Didier Raoult’s powerful French studies favoring HCQ efficacy were not randomized, Risch argued that the results were, nevertheless, so stunning as to far outweigh that deficit: “The first study of HCQ + AZ [ . . . ] showed a 50-fold benefit of HCQ + AZ vs. standard of care . . . This is such an enormous difference that it cannot be ignored despite lack of randomization.”32 Risch has pointed out that the supposed need for randomized placebo-controlled trials is a shibboleth. In 2014 the Cochrane Collaboration proved in a landmark meta-analysis of 10,000 studies, that observational studies of the kind produced by Didier Raoult are equal

if consumption by the one billion people in the developed countries declined, it is certainly nowhere close to doing so where the other six billion of us are concerned. If the rest of the world bought cars and trucks at the same per capita rate as in the United States, the world’s population of cars and trucks would be 5.5 billion. The production of global warming pollution and the consumption of oil would increase dramatically over and above today’s unsustainable levels. With the increasing population and rising living standards in developing countries, the pressure on resource constraints will continue, even as robosourcing and outsourcing reduce macroeconomic demand in developed countries. Around the same time that The Limits to Growth was published, peak oil production was passed in the United States. Years earlier, a respected geologist named M. King Hubbert collected voluminous data on oil production in the United States and calculated that an immutable peak would be reached shortly after 1970. Although his predictions were widely dismissed, peak production did occur exactly when he predicted it would. Exploration, drilling, and recovery technologies have since advanced significantly and U.S. oil production may soon edge back slightly above the 1970 peak, but the new supplies are far more expensive. The balance of geopolitical power shifted slightly after the 1970 milestone. Less than a year after peak oil production in the U.S., the Organization of Petroleum Exporting Countries (OPEC) began to flex its muscles, and two years later, in the fall of 1973, the Arab members of OPEC implemented the first oil embargo. Since those tumultuous years when peak oil was reached in the United States, energy consumption worldwide has doubled, and the growth rates in China and other emerging markets portend further significant increases. Although the use of coal is declining in the U.S., and coal-fired generating plants are being phased out in many other developed countries as well, China’s coal imports have already increased 60-fold over the past decade—and will double again by 2015. The burning of coal in much of the rest of the developing world has also continued to increase significantly. According to the International Energy Agency, developing and emerging markets will account for all of the net global increase in both coal and oil consumption through the next two decades. The prediction of global peak oil is fraught with

As Graedon scrutinized the FDA’s standards for bioequivalence and the data that companies had to submit, he found that generics were much less equivalent than commonly assumed. The FDA’s statistical formula that defined bioequivalence as a range—a generic drug’s concentration in the blood could not fall below 80 percent or rise above 125 percent of the brand name’s concentration, using a 90 percent confidence interval—still allowed for a potential outside range of 45 percent among generics labeled as being the same. Patients getting switched from one generic to another might be on the low end one day, the high end the next. The FDA allowed drug companies to use different additional ingredients, known as excipients, that could be of lower quality. Those differences could affect a drug’s bioavailability, the amount of drug potentially absorbed into the bloodstream. But there was another problem that really drew Graedon’s attention. Generic drug companies submitted the results of patients’ blood tests in the form of bioequivalence curves. The graphs consisted of a vertical axis called Cmax, which mapped the maximum concentration of drug in the blood, and a horizontal axis called Tmax, the time to maximum concentration. The resulting curve looked like an upside-down U. The FDA was using the highest point on that curve, peak drug concentration, to assess the rate of absorption into the blood. But peak drug concentration, the point at which the blood had absorbed the largest amount of drug, was a single number at one point in time. The FDA was using that point as a stand-in for “rate of absorption.” So long as the generic hit a similar peak of drug concentration in the blood as the brand name, it could be deemed bioequivalent, even if the two curves reflecting the time to that peak looked totally different. Two different curves indicated two entirely different experiences in the body, Graedon realized. The measurement of time to maximum concentration, the horizontal axis, was crucial for time-release drugs, which had not been widely available when the FDA first created its bioequivalence standard in 1992. That standard had not been meaningfully updated since then. “The time to Tmax can vary all over the place and they don’t give a damn,” Graedon emailed a reporter. That “seems pretty bizarre to us.” Though the FDA asserted that it wouldn’t approve generics with “clinically significant” differences in release rates, the agency didn’t disclose data filed by the companies, so it was impossible to know how dramatic the differences were.

Punishment is not care, and poverty is not a crime. We need to create safe, supportive pathways for reentry into the community for all people and especially young people who are left out and act out. Interventions like decriminalizing youthful indiscretions for juvenile offenders and providing foster children and their families with targeted services and support would require significant investment and deliberate collaboration at the community, state, and federal levels, as well as a concerted commitment to dismantling our carceral state. These interventions happen automatically and privately for young offenders who are not poor, whose families can access treatment and hire help, and who have the privilege of living and making mistakes in neighborhoods that are not over-policed. We need to provide, not punish, and to foster belonging and self-sufficiency for our neighbors’ kids. More, funded YMCAs and community centers and summer jobs, for example, would help do this. These kinds of interventions would benefit all the Carloses, Wesleys, Haydens, Franks, and Leons, and would benefit our collective well-being. Only if we consider ourselves bound together can we reimagine our obligation to each other as community. When we consider ourselves bound together in community, the radically civil act of redistributing resources from tables with more to tables with less is not charity, it is responsibility; it is the beginning of reparation. Here is where I tell you that we can change this story, now. If we seek to repair systemic inequalities, we cannot do it with hope and prayers; we have to build beyond the systems and begin not with rehabilitation but prevention. We must reimagine our communities, redistribute our wealth, and give our neighbors access to what they need to live healthy, sustainable lives, too. This means more generous social benefits. This means access to affordable housing, well-resourced public schools, affordable healthcare, jobs, and a higher minimum wage, and, of course, plenty of good food. People ask me what educational policy reform I would suggest investing time and money in, if I had to pick only one. I am tempted to talk about curriculum and literacy, or teacher preparation and salary, to challenge whether police belong in schools, to push back on standardized testing, or maybe debate vocational education and reiterate that educational policy is housing policy and that we cannot consider one without the other. Instead, as a place to start, I say free breakfast and lunch. A singular reform that would benefit all students is the provision of good, free food at school. (Data show that this practice yields positive results; but do we need data to know this?) Imagine what would happen if, across our communities, people had enough to feel fed.

For a well-defined, standard, and stable process involving hand-offs between people and systems, it is preferable to use a smart workflow platform. Such platforms offer pre-developed modules. These are ready-to-use automation programs customized by industry and by business function (e.g., onboarding of clients in retail banking). In addition, they are modular. For example, a module might include a form for client data collection, and another module might support an approval workflow. In addition, these modules can be linked to external systems and databases using connectors, such as application programming interfaces (APIs), which enable resilient data connectivity. Hence, with smart workflows, there is no need to develop bespoke internal and external data bridges. This integration creates a system with high resiliency and integrity. In addition, the standardization by industry and function of these platforms, combined with the low-code functionality, helps to accelerate the implementation.

There are no ready-to-use modules with RPA. Most of the development is bespoke, and all process flows need to be built almost from scratch. The connections also need to be constructed. This results in a more flexible design and implementation of the programs developed, which can fit with more specific business requirements. The key advantage of RPA is that it allows the creation of automation programs that can involve legacy systems (e.g., those which can’t use APIs) or address non-standard requirements (e.g., onboarding of clients for a broker insurance company under Singapore regulations). However, with RPA, the lack of native integration amongst the components has weaknesses. For example, it involves less robustness, weaker data integrity, and lower resilience to process changes. If one part of an RPA program fails, the whole end-to-end process is stopped. As an outcome, based on our experience, the leading practice is to use low-code and smart workflow platforms as a foundation of the overall automation platform. In contrast, RPA is used for any integration of the overall platform with legacy systems or for automation of bespoke processes.

It’s tempting to think that the male bias that is embedded in language is simply a relic of more regressive times, but the evidence does not point that way. The world’s ‘fastest-growing language’,34 used by more than 90% of the world’s online population, is emoji.35 This language originated in Japan in the 1980s and women are its heaviest users:36 78% of women versus 60% of men frequently use emoji.37 And yet, until 2016, the world of emojis was curiously male. The emojis we have on our smartphones are chosen by the rather grand-sounding ‘Unicode Consortium’, a Silicon Valley-based group of organisations that work together to ensure universal, international software standards. If Unicode decides a particular emoji (say ‘spy’) should be added to the current stable, they will decide on the code that should be used. Each phone manufacturer (or platform such as Twitter and Facebook) will then design their own interpretation of what a ‘spy’ looks like. But they will all use the same code, so that when users communicate between different platforms, they are broadly all saying the same thing. An emoji face with heart eyes is an emoji face with heart eyes. Unicode has not historically specified the gender for most emoji characters. The emoji that most platforms originally represented as a man running, was not called ‘man running’. It was just called ‘runner’. Similarly the original emoji for police officer was described by Unicode as ‘police officer’, not ‘policeman’. It was the individual platforms that all interpreted these gender-neutral terms as male. In 2016, Unicode decided to do something about this. Abandoning their previously ‘neutral’ gender stance, they decided to explicitly gender all emojis that depicted people.38 So instead of ‘runner’ which had been universally represented as ‘male runner’, Unicode issued code for explicitly male runner and explicitly female runner. Male and female options now exist for all professions and athletes. It’s a small victory, but a significant one.

The reason women are more likely to have to transfer is because, like most cities around the world, London’s public transport system is radial.29 What this means is that a single ‘downtown’ area has been identified and the majority of routes lead there. There will be some circular routes, concentrated in the centre. The whole thing looks rather like a spider’s web, and it is incredibly useful for commuters, who just want to get in and out of the centre of town. It is, however, less useful for everything else. And this useful/not so useful binary falls rather neatly onto the male/female binary. But while solutions like London’s hopper fare are an improvement, they are by no means standard practice worldwide. In the US, while some cities have abandoned charging for transfers (LA stopped doing this in 2014), others are sticking with it.30 Chicago for example, still charges for public transport connections.31 These charges seem particularly egregious in light of a 2016 study which revealed quite how much Chicago’s transport system is biased against typical female travel patterns.32 The study, which compared Uberpool (the car-sharing version of the popular taxi app) with public transport in Chicago, revealed that for trips downtown, the difference in time between Uberpool and public transport was negligible – around six minutes on average. But for trips between neighbourhoods, i.e. the type of travel women are likely to be making for informal work or care-giving responsibilities, Uberpool took twenty-eight minutes to make a trip that took forty-seven minutes on public transport.

Let’s start with the assumption that all members of a household enjoy an equal standard of living. Measuring poverty by household means that we lack individual level data, but in the late 1970s, the UK government inadvertently created a handy natural experiment that allowed researchers to test the assumption using a proxy measure.16 Until 1977, child benefit in Britain was mainly credited to the father in the form of a tax reduction on his salary. After 1977 this tax deduction was replaced by a cash payment to the mother, representing a substantial redistribution of income from men to women. If money were shared equally within households, this transfer of income ‘from wallet to purse’ should have had no impact on how the money was spent. But it did. Using the proxy measure of how much Britain was spending on clothes, the researchers found that following the policy change the country saw ‘a substantial increase in spending on women’s and children’s clothing, relative to men’s clothing’.

The standard deviation is the descriptive statistic that allows us to assign a single number to this dispersion around the mean.

Driver Behavior & Safety Proper driving behavior is vital for the safety of drivers, passengers, pedestrians and is a means to achieve fewer road accidents, injuries and damage to vehicles. It plays a role in the cost of managing a fleet as it impacts fuel consumption, insurance rates, car maintenance and fines. It is also important for protecting a firm’s brand and reputation as most company- owned vehicles carry the company’s logo. Ituran’s solution for driver behavior and safety improves organizational driving culture and standards by encouraging safer and more responsible driving. The system which tracks and monitors driver behavior using an innovative multidimensional accelerometer sensor, produces (for each driver) an individual score based on their performance – sudden braking and acceleration, sharp turns, high-speed driving over speed bumps, erratic overtaking, speeding and more. The score allows fleet managers to compare driver performance, set safety benchmarks and hold each driver accountable for their action. Real-time monitoring identifies abnormal behavior mode—aggressive or dangerous—and alerts the driver using buzzer or human voice indication, and detects accidents in real time. When incidents or accidents occurs, a notification sent to a predefined recipient alerts management, and data collected both before and after accidents is automatically saved for future analysis. • Monitoring is provided through a dedicated application which is available to both fleet manager and driver (with different permission levels), allowing both to learn and improve • Improves organizational driving culture and standards and increases safety of drivers and passengers • Web-based reporting gives a birds-eye view of real-time driver data, especially in case of an accident • Detailed reports per individual driver include map references to where incidents have occurred • Comparative evaluation ranks driving according to several factors; the system automatically generates scores and a periodic assessment certificate for each driver and/or department Highlights 1. Measures and scores driver performance and allows to give personal motivational incentives 2. Improves driving culture by encouraging safer and more responsible driving throughout the organization 3. Minimizes the occurrence of accidents and protects the fleet from unnecessary wear & tear 4. Reduces expenses related to unsafe and unlawful driving: insurance, traffic tickets and fines See how it works:

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Unfortunately, the SQL standard’s definition of isolation levels is flawed—it is ambiguous, imprecise, and not as implementation-independent as a standard should be [28]. Even though several databases implement repeatable read, there are big differences in the guarantees they actually provide, despite being ostensibly standardized [23]. There has been a formal definition of repeatable read in the research literature [29, 30], but most implementations don’t satisfy that formal definition. And to top it off, IBM DB2 uses “repeatable read” to refer to serializability [8]. As a result, nobody really knows what repeatable read means.

[원료약품분량] 이 약 1정(126mg) 중 졸피뎀 타르타르산염 (EP) [성상] 백색 장방형의 필름코팅제 [효능효과] 불면증 [용법용량] 까톡【pak6】텔레:【JRJR331】텔레:【TTZZZ6】라인【TTZZ6】 졸피뎀(Zolpidem) 은 불면증이나 앰비엔(Ambien), 앰비엔 CR(Ambien CR), 인터메조(Intermezzo), 스틸넉스(Stilnox), 스틸넉트(Stilnoct), 서블리넉스(Sublinox), 하이프너젠(Hypnogen), 조네이딘(Zonadin), Sanval, Zolsana and Zolfresh 등은 졸피뎀의 시판되는 품명이다. 1) 이 약은 작용발현이 빠르므로, 취침 바로 직전에 경구투여한다. In addition to "I love you" used to date in Korean, there are old words such as "goeda" [3], "dada" [4], and "alluda" [5]. In Chinese characters, 愛(ae) and 戀(yeon) have the meaning of love. In Chinese characters, 戀 mainly means love in a relationship, and 愛 means more comprehensive love than that. In the case of Jeong, the meaning is more comprehensive than Ae or Yeon, and it is difficult to say the word love. In the case of Japanese, it is divided into two types: 愛 (あい) and 恋 (いこ) [6]. There are two main views on etymology. First of all, there is a hypothesis that the combination of "sal" in "live" or "sard" and the suffix "-ang"/"ung" was changed to "love" from the Middle Ages, but "love" clearly appears as a form of "sudah" in the Middle Ages, so there is a problem that the vowels do not match at all. Although "Sarda" was "Sanda," the vowels match, but the gap between "Bulsa" and "I love you" is significant, and "Sanda" and "Sanda Lang," which were giants, have a difference in tone, so it is difficult to regard it as a very reliable etymology. Next, there is a hypothesis that it originated from "Saryang," which means counting the other person. It is a hypothesis argued by Korean language scholars such as Yang Ju-dong, and at first glance, it can be considered that "Saryang," which means "thinking and counting," has not much to do with "love" in meaning. In addition, some criticize the hypothesis, saying that the Chinese word Saryang itself is an unnatural coined word that means nothing more than "the amount of thinking." However, in addition to the meaning of "Yang," there is a meaning of "hearida," and "Saryang" is also included in the Standard Korean Dictionary and the Korean-Chinese Dictionary as a complex verb meaning "think and count." In addition, as will be described later, Saryang is an expression whose history is long enough to be questioned in the Chinese conversation book "Translation Noguldae" in the early 16th century, so the criticism cannot be considered to be consistent with the facts. In addition, if you look at the medieval Korean literature data, you can find new facts. 2) 성인의 1일 권장량은 10mg이며, 이러한 권장량을 초과하여서는 안된다. 노인 또는 쇠약한 환자들의 경우, 이 약의 효과에 민감할 수 있기 때문에, 권장량을 5mg으로 하며, 1일 10mg을 초과하지 않는다. ^^바로구입가기^^ ↓↓아래 이미지 클릭↓↓ 까톡【pak6】텔레:【JRJR331】텔레:【TTZZZ6】라인【TTZZ6】 3) 간 손상으로 이 약의 대사 및 배설이 감소될 수 있으므로, 노인 환자들에서처럼 특별한 주 의와 함께 용량을 5mg에서 시작하도록 한다. 4) 65세 미만의 성인의 경우, 약물의 순응도가 좋으면서 임상적 반응이 불충분한 경우 용량을 10mg까지 증량할 수 있다. 5) 치료기간은 보통 수 일에서 2주, 최대한 4주까지 다양하며, 용량은 임상적으로 적절한 경우 점진적으로 감량해가도록 한다. 6) 다른 수면제들과 마찬가지로, 장기간 사용은 권장되지 않으며, 1회 치료기간은 4주를 넘지 않도록 한다.

I will remind you of what the central limit theorem tells us: (1) the sample means for any population will be distributed roughly as a normal distribution around the true population mean; (2) we would expect the sample mean and the sample standard deviation to be roughly equal to the mean and standard deviation for the population from which it is drawn; and (3) roughly 68 percent of sample means will lie within one standard error of the population mean, roughly 95 percent will lie within two standard errors of the population mean, and so on. In less technical language, this all means that any sample should look a lot like the population from which it is drawn; while every sample will be different, it would be relatively rare for the mean of a properly drawn sample to deviate by a huge amount from the mean for the relevant underlying population. Similarly, we would also expect two samples drawn from the same population to look a lot like each other. Or, to think about the situation somewhat differently, if we have two samples that have extremely dissimilar means, the most likely explanation is that they came from different populations.

For this reason, the “gold standard” of research is randomization, a process by which human subjects (or schools, or hospitals, or whatever we’re studying) are randomly assigned to either the treatment or the control group. We do not assume that all the experimental subjects are identical. Instead, probability becomes our friend (once again), and we assume that randomization will evenly divide all relevant characteristics between the two groups—both the characteristics we can observe, like race or income, but also confounding characteristics that we cannot measure or had not considered, such as perseverance or faith.

The standard error measures the dispersion of the sample means. How tightly do we expect the sample means to cluster around the population mean? There is some potential confusion here, as we have now introduced two different measures of dispersion: the standard deviation and the standard error. Here is what you need to remember to keep them straight: 1. The standard deviation measures dispersion in the underlying population. In this case, it might measure the dispersion of the weights of all the participants in the Framingham Heart Study, or the dispersion around the mean for the entire marathon field. 2. The standard error measures the dispersion of the sample means. If we draw repeated samples of 100 participants from the Framingham Heart Study, what will the dispersion of those sample means look like? 3. Here is what ties the two concepts together: The standard error is the standard deviation of the sample means! Isn’t that kind of cool?

Once we get the regression results, we would calculate a t-statistic, which is the ratio of the observed coefficient to the standard error for that coefficient.* This t-statistic is then evaluated against whatever t-distribution is appropriate for the size of the data sample (since this is largely what determines the number of degrees of freedom). When the t-statistic is sufficiently large, meaning that our observed coefficient is far from what the null hypothesis would predict, we can reject the null hypothesis at some level of statistical significance. Again, this is the same basic process of statistical inference that we have been employing throughout the book. The fewer the degrees of freedom (and therefore the “fatter” the tails of the relevant t-distribution), the higher the t-statistic will have to be in order for us to reject the null hypothesis at some given level of significance. In the hypothetical regression example described above, if we had four degrees of freedom, we would need a t-statistic of at least 2.13 to reject the null hypothesis at the .05 level (in a one-tailed test). However, if we have 20,000 degrees of freedom (which essentially allows us to use the normal distribution), we would need only a t-statistic of 1.65 to reject the null hypothesis at the .05 level in the same one-tailed test.

With your new .01 standard error, the 95 percent confidence intervals for the candidates are the following: Republican: 52 ± 2, or between 50 and 54 percent of the votes cast; Democrat: 45 ± 2, or between 43 and 47 percent of the votes cast. There is no longer any overlap between the two confidence intervals. You can predict on air that the Republican candidate is the winner; more than 95 times out of 100 you will be correct.*

Even the results of clinical trials, which are usually randomized experiments and therefore the gold standard of medical research, should be viewed with some skepticism. In 2011, the Wall Street Journal ran a front-page story on what it described as one of the “dirty little secrets” of medical research: “Most results, including those that appear in top-flight peer-reviewed journals, can’t be reproduced.”7

For a scientist, the only valid question is to decide whether the phenomenon can be studied by itself, or whether it is an instance of a deeper problem. This book attempts to illustrate, and only to illustrate, the latter approach. And my conclusion is that, through the UFO phenomenon, we have the unique opportunities to observe folklore in the making and to gather scientific material at the deepest source of human imagination. We will be the object of much contempt by future students of our civilization if we allow this material to be lost, for "tradition is a meteor which, once it falls, cannot be rekindled." If we decide to avoid extreme speculation, but make certain basic observations from the existing data, five principal facts stand out rather clearly from our analysis so far: Fact 1. There has been among the public, in all countries, since the middle of 1946, an extremely active generation of colorful rumors. They center on a considerable number of observations of unknown machines close to the ground in rural areas, the physical traces left by these machines, and their various effects on humans and animals. Fact 2. When the underlying archetypes are extracted from these rumors, the extraterrestrial myth is seen to coincide to a remarkable degree with the fairy-faith of Celtic countries, the observations of the scholars of past ages, and the widespread belief among all peoples concerning entities whose physical and psychological description place them in the same category as the present-day ufonauts. Fact 3. The entities human witnesses report to have seen, heard, and touched fall into various biological types. Among them are beings of giant stature, men indistinguishable from us, winged creatures, and various types of monsters. Most of the so-called pilots, however, are dwarfs and form two main groups: (1) dark, hairy beings – identical to the gnomes of medieval theory – with small, bright eyes and deep, rugged, "old" voices; and (2) beings – who answer the description of the sylphs of the Middle Ages or the elves of the fairy-faith – with human complexions, oversized heads, and silvery voices. All the beings have been described with and without breathing apparatus. Beings of various categories have been reported together. The overwhelming majority are humanoid. Fact 4. The entities' reported behavior is as consistently absurd as the appearance of their craft is ludicrous. In numerous instances of verbal communications with them, their assertions have been systematically misleading. This is true for all cases on record, from encounters with the Gentry in the British Isles to conversations with airship engineers during the 1897 Midwest flap and discussions with the alleged Martians in Europe, North and South America, and elsewhere. This absurd behavior has had the effect of keeping professional scientists away from the area where that activity was taking place. It has also served to give the saucer myth its religious and mystical overtones. Fact 5. The mechanism of the apparitions, in legendary, historical, and modern times, is standard and follows the model of religious miracles. Several cases, which bear the official stamp of the Catholic Church (such as those in Fatima and Guadalupe), are in fact – if one applies the deffinitions strictly – nothing more than UFO phenomena where the entity has delivered a message having to do with religious beliefs rather than with space or engineering.

the cigarette companies were already aware that the science was starting to look pretty bad for them. They met to figure out how to respond to this looming crisis. Their answer was—alas—quite brilliant, and it set the standard for propaganda ever since. They muddied the waters.

How can you run Analytics “as one”? If you leave Analytics to IT, you will end up with a first-class race car without a driver: All the technology would be there, but hardly anybody could apply it to real-world questions. Where Analytics is left to Business, however, you’d probably see various functional silos develop, especially in larger organizations. I have never seen a self-organized, cross-functional Analytics approach take shape successfully in such an organization. Instead, you can expect each Analytics silo to develop independently. They will have experts familiar with their business area, which allows for the right questions to be asked. On the other hand, the technical solutions will probably be second class as the functional Analytics department will mostly lack the critical mass to mimic an organization’s entire IT intelligence. Furthermore, a lot of business topics will be addressed several times in parallel, as those Analytics silos may not talk to each other. You see this frequently in organizations that are too big for one central management team. They subdivide management either into functional groups or geographical groups. Federation is generally seen as an organizational necessity. It is well known that it does not make sense to regularly gather dozens of managers around the same table: You’d quickly see a small group discussing topics that are specific to a business function or a country organization, while the rest would get bored. A federated approach in Analytics, however, comes with risks. The list of disadvantages reaches from duplicate work to inconsistent interpretation of data. You can avoid these disadvantages by designing a central Data Analytics entity as part of your Data Office at an early stage, to create a common basis across all of these areas. As you can imagine, such a design requires authority, as it would ask functional silos to give up part of their autonomy. That is why it is worthwhile creating a story around this for your organization’s Management Board. You’d describe the current setup, the behavior it fosters, and the consequences including their financial impact. Then you’d present a governance structure that would address the situation and make the organization “future-proof.” Typical aspects of such a proposal would be The role of IT as the entity with a monopoly for technology and with the obligation to consider the Analytics teams of the business functions as their customers The necessity for common data standards across all of those silos, including their responsibility within the Data Office Central coordination of data knowledge management, including training, sharing of experience, joint cross-silo expert groups, and projects Organization-wide, business-driven priorities in Data Analytics Collaboration bodies to bring all silos together on all management levels

In the University of Texas study, for example, the researchers came up with the theory that big-butted women were better able to forage for food, but they provided no experimental data to back it up. This is a fundamental problem that many biologists have with evolutionary psychology: it doesn’t adhere to the standards of other sciences that study biological evolution.

So when you come across some observations that do not fit the standard explanation, let your mind wander to see whether some radically different interpretation might do a better job. Perhaps you will think of something that will fit both the new data and the old data and thereby supplant the standard expla- nation. Toy with different perspectives. Look for the unusual. Try consciously to innovate. Train yourself to imagine new schemes and innovative ways to fit the pieces together. Seek the joy of discovery. Always test your new thoughts against the facts, of course, in rigorous, cold-blooded, unemotional scientific manner. But play the great game of the visionary and the innovator as well.

Many other industries have their practice patterns measured. In 2009, the utility company Positive Energy (now Opower) was interested in reducing power use in neighborhoods. Their data showed that some households used far more electricity than their neighbors. After all, there are no standardized protocols on turning lights on or off when one vacates a room. Just ask anyone who’s argued with a spouse about this issue. The company decided to mail each household a regular feedback report that compared their electricity and natural gas usage to that of similarly sized households in their neighborhood. Playing on the benchmarking theme, the data feedback intervention resulted in an overall reduction in household energy use. When people saw they were outliers, they modified their habits so their usage fell more into line with that of their peers. In a year, this simple intervention reduced the total carbon emissions of the participating houses by the equivalent of 14.3 million gallons of gasoline, saving consumers more than $20 million.4 Lots of utility companies now take this approach—and it works.

This is a moral as well as an aesthetic question. If natural beauty and historic interest can be seen as mere 'nostalgic figments,' so can moral standards and all the traditional interests of human beings, until we cease to be men and become laboratory data.

It is true that vaccines have in the past taken a long, long time to develop. Until 2020, a new vaccine usually took at least ten years to develop from concept to roll-out. Many took much longer. The malaria vaccine programme at the Jenner Institute has been going for twenty-five years, and research into malaria vaccines had been going on for more than a hundred years – so far, with limited success. The lab-to-jab record-holder was the mumps vaccine, developed in four years by Maurice Hilleman in the United States in the 1960s.1 But the standard lengthy timeline we were all used to was never because vaccine development required ten, fifteen or thirty years of continuous painstaking lab work, clinical trials and data analysis. For every vaccine that had ever been developed up until 2020, most of the elapsed development time was spent waiting. In 2020, there were three key factors that enabled us to cut out the waiting and crunch ten years into one: first, the work we had already done; second, changes to the way funding was given out; and third, doing in parallel things that we would normally do in sequence.

Onora O’Neill argues that if we want to demonstrate trustworthiness, we need the basis of our decisions to be “intelligently open.” She proposes a checklist of four properties that intelligently open decisions should have. Information should be accessible: that implies it’s not hiding deep in some secret data vault. Decisions should be understandable—capable of being explained clearly and in plain language. Information should be usable—which may mean something as simple as making data available in a standard digital format. And decisions should be assessable—meaning that anyone with the time and expertise has the detail required to rigorously test any claims or decisions if they wish to.

School administrators are key players in analysing data for accountability, ensuring educational standards are met, and fostering continuous improvement.

Nevertheless, they felt a powerful urge to impart their wisdom to their friends at ARPA. Thanks to the legal beagles’ strictures, they were reduced to getting their points across by a weird pantomime of asking inscrutable but cunningly pointed questions. “Somebody would be talking about the design for some element and we’d drop all these hints,” Shoch recalled. “We’d say, ‘You know, that’s interesting, but what happens if this error message comes back, and what happens if that’s followed by a delayed duplicate that was slowed down in its response from a distant gateway when the flow control wouldn’t take it but it worked its way back and got here late? What do you do then?’ There would be this pause and they’d say, ‘You’ve tried this!’ And we’d reply, ‘Hey, we never said that!’” Eventually they managed to communicate enough of Pup’s architecture for it to become a crucial part of the ARPANET standard known as TCP/IP, which to this date is what enables data packets to pass gracefully across the global data network known as the Internet—with a capital “I.

Okay, check this. The fact that there are 52 cards in a standard deck doesn’t mean very much. It isn’t a very interesting fact on its own. The fact that there are also 52 weeks in a year could be a mere coincidence. Except there are also four suits in a deck and four seasons in a year, thirteen cards in a suit and thirteen lunar cycles in a year, and if you count all the symbols in a deck they add up to 365. So, a fact is knowing that there are 52 cards in a standard playing card deck, but a good trivia fact is knowing that the 52-card deck’s design was based on a calendar year. Trivia takes this boring piece of information and makes it . . . magical. It makes meaning out of raw data, the way statistics does with numbers, except trivia does it with all the loose odds and ends of the universe.

the proliferation of user-developed spreadsheets and databases inevitably leads to multiple versions of key indicators within an organization. Furthermore, research has shown that between 20% and 40% of spreadsheets contain errors; the more spreadsheets floating around a company, therefore, the more fecund the breeding ground for mistakes. Analytics competitors, by contrast, field centralized groups to ensure that critical data and other resources are well managed and that different parts of the organization can share data easily, without the impediments of inconsistent formats, definitions, and standards.

Farm employees, their families, and consumers are protected from dangerous and persistent Organic Products found on the farm and in food, as well as in the land they work and play on, the air they breathe, and the water they drink, by using organic products. Children are particularly vulnerable to pollutants. As a result of the formation of organic food and feed items into the marketplace, parents may simply select products that are free of these chemicals. Hair Care Product Natural grown foods are higher in minerals like Vitamin C, iron, magnesium, and phosphorus, but have lower amounts of nitrates and pesticide residues when compared to conventionally grown foods, according to mounting data. Taking care of it properly is one of the simplest promoting short - to - medium healing processes and brightness. Organic Skin Care products, in particular, combine essential vitamins, herbs, and minerals to cure and regenerate our skin while causing the least amount of environmental damage. How do reduce hair fall so I stop my hair from falling out? These natural skincare companies are dedicated to altering the beauty industry's standards for products that are beneficial both to us and the environment for hair growth which oil is best. We admire their commitment to maximum potency, freshness, and complete purity! In Ayurveda, bhringraj oil is a natural treatment for restoring the look of fine wrinkles (Ayurvedic medicine medicine). Bhringraj oil is often used to increase hair growth, gloss, softness, and strength and is thought to prevent undesired greying and hair growth. Ayurvedic practitioners also advise consuming bhringraj oil orally to treat everything from heart disease and respiratory issues to neurological and liver issues. You're not sure which soap is best for dry skin. Sensitive skin is difficult to deal with. Which is the best soap for dry skin patients may notice tightness and pallor even in the summer, so forget about winter dryness! Warm showers, as well as unsuitable soap, such as aloe vera, Aloe vera face mist, for example, could aggravate the issue. You can apply an after-shower lotion and emollients to keep your skin hydrated. Contact us: Arendelle Organics NRK BizPark, Behind C21 Mall, Scheme 54 PU4, Indore, Madhya Pradesh, India 8109099301 care@arendelleorganics.com

CPU clock speeds are barely increasing, but multi-core processors are standard, and networks are getting faster. This means parallelism is only going to increase.

The report acknowledges that, “The BMGF developed a model of chloroquine penetration into tissues for malaria.”69 BMGF’s unique dosing model for the studies deliberately overestimated the amount of HCQ that necessary to achieve adequate lung tissue concentrations. The WHO report confesses that, “This model is however not validated.” Gates’s deadly deception allowed FDA to wrongly declare that HCQ would be ineffective at safe levels. The minutes of that March 13, 2020 meeting suggest that BMGF knew the proper drug dosing and the need for early administration. Yet their same researchers then participated in deliberately providing a potentially lethal dose, failing to dose by weight, missing the early window during which treatment was known to be effective, and giving the drug to subjects who were already critically ill with comorbidities that made it more likely they would not tolerate the high dose. The Solidarity trial design also departed from standard protocols by collecting no safety data: only whether the patient died, or how many days they were hospitalized. Researchers collected no information on in-hospital complications. This strategy shielded the WHO from gathering information that could pin adverse reactions on the dose.

He said he simply could not recommend a drug until he saw “randomized, blinded, placebo-controlled trial” results. That was the “gold standard,” he said. It would be that, or nothing. When they asked him, “Why not?” he shouted, “There’s no data!”42 He told them that the treatment experiences and voluminous case study reports of dozens of community AIDS doctors was not real science.

Thirdly, pragmatism is impractical for the reason that standard intellectual problems in the history of knowledge are among those which we encounter in our environment and trouble us, and yet pragmatism arbitrarily relegates them to the classification of impertinence. But why should social reform be worthy of inquiry, but overcoming skepticism's nagging difficulties ignored? Intellectual problems are just as real problems as other kinds. Therefore, we can ask just how well Dewey's viewpoint 'works' if it fails to give us a coherent and unified conceptual mastery over the data of experience. On this score, pragmatism must be rated quite low, for the coherence of Dewey's philosophy can be seriously questioned.

Basically put, the "multiple souls" or the "two souls" model posits that each person is a triad of communicating, relating powers- a body alongside two souls- a breath soul, and a free soul (also called a wandering soul.) These terms have become standard anthropological terms used in studies of primal anthropologies. Several of the above authors mentioned utilize these terms in their own studies. I will commence now in describing the two-souls perspective. It will be important to quote Merkur here, before I begin. "In all, Soul dualism is an ideological system, designed not to describe but to explain psychological phenomenon. It is a system of psychology that is based on phenomenological data of psychic experience and systematized through philosophical speculation.

We read in order to live our true selves, not just get information that we can use to raise our standard of living. Bible reading is a means of listening to and obeying God, not gathering religious data by which we can be our own gods.

Just try this thought experiment: Imagine that it’s 1993. The Web is just appearing. And imagine that you—an unusually prescient type—were to explain to people what they could expect by, say, the summer of 2003. Universal access to practically all information. From all over the place—even in bars. And all for free! I can imagine the questions the skeptics would have asked: How will this be implemented? How will all of this information be digitized and made available? (Lots of examples along the line of “a thousand librarians with scanners would take fifty years to put even a part of the Library of Congress online, and who would pay for that?”) Lots of questions about how people would agree on standards for wireless data transmission—“It usually takes ten years just to develop a standard, much less put it into the market-place!”— and so on, and so on.

Now that I think about it, I once had a debate with him about how completely you should or shouldn’t isolate the cardholder data environment in order to comply with the PCI Data Security Standard.

As Quine has said, 'Our statements about the external world face the tribunal of sense experience not individually but only as a corporate body.' Quine has uncovered an important feature of human thought here, one that the analytic-synthetic distinction overlooks. Beliefs are not held or tested atomistically, in a one-by-one fashion. Rather beliefs come in clusters, forming a worldview, which is then used to interpret experience. One's worldview as a whole confronts the data of language and experience. Simple appeals to language (analyticity) or observation (syntheticity) do not reveal whether or not our beliefs will be altered when challenged. People will grant revisionary immunity to certain core beliefs not on the basis of linguistics, but on the basis of an overall worldview. Those beliefs that are held most dearly and form the hearts of one's conceptual scheme will be the hardest to let go and the last we let go. Indeed, all of us have beliefs we will cling to regardless of almost anything! Only a revolution in our conceptual framework will alter these most central beliefs. These beliefs often appear to be almost insulated from testing; indeed, they are the standard by which everything else is tested...This is not to say that all human beings think in consistent systems, or even that most are aware of the nature of their own thought. It is virtually certain that everyone holds to some incompatible or inconsistent beliefs. Most people do not reflect on their own thinking enough to notice these inconsistencies or to notice the 'worldviewesh' character of their thought.

How to Use the Biker Service for CNIC Applications In an effort to streamline the application process for CNICs (Computerized National Identity Cards) and make government services more accessible, the Biker Service has been introduced as a part of Pakistan’s initiative to modernize its registration systems. This service allows citizens to complete their CNIC applications from the comfort of their homes through mobile service officers who assist in the process. Here’s a guide on how to effectively use the Biker Service for your CNIC application. 1. What is the Biker Service? The Biker Service is an outreach program that sends mobile registration officers directly to applicants’ homes. This service is especially beneficial for individuals who face difficulties visiting local NADRA (National Database and Registration Authority) centers due to health, age, or mobility issues. It provides doorstep assistance to ensure all parts of the CNIC application, including biometric verification and photo capture, are done seamlessly. 2. Key Benefits Convenience: Eliminates the need to visit physical centers. Time-Saving: Reduces waiting times associated with in-person visits. Accessibility: Helps those in remote or underserved areas complete their applications. Professional Assistance: Ensures the application is correctly filled and all necessary steps are completed. 3. Steps to Use the Biker Service for CNIC Applications Booking an Appointment: Contact NADRA’s helpline or visit their online service portal to schedule a biker appointment. Provide your personal details and address to confirm the visit. Prepare Required Documents: Ensure that you have all necessary documents ready, such as your old CNIC (if renewing), a copy of your birth certificate, or any relevant supporting paperwork for a new CNIC application. The Biker Visit: On the scheduled day, a NADRA officer on a motorcycle will arrive at your address. The officer will verify your identity, take your photograph, and collect your biometric data (fingerprints and thumb impressions) directly at your home. Completion and Confirmation: The mobile officer submits your application to the NADRA system. You will receive a receipt or application tracking number for follow-up. Payment and Fees: The cost of the Biker Service includes standard CNIC processing fees plus an additional convenience charge. Ensure you have the means to make a payment, whether via cash or through any payment method recommended during the booking. 4. Tracking Your Application Once your application is submitted, you can track its progress using NADRA’s online tracking system. Enter your application or tracking number to check the status and receive updates on when your CNIC will be ready for collection or delivery. Final Thoughts The Biker Service by NADRA is a major advancement in making government services more user-friendly and accessible to a broader population. It saves applicants from the hassle of traveling and long queues, making it especially useful for senior citizens, individuals with disabilities, and those living in remote areas. If you’re eligible and looking for a hassle-free way to apply for a CNIC, this service is a great option to consider.