Accurate Data Quotes

In deep learning, there’s no data like more data. The more examples of a given phenomenon a network is exposed to, the more accurately it can pick out patterns and identify things in the real world.

When the tragedies of others become for us diversions, sad stories with which to enthrall our friends, interesting bits of data to toss out at cocktail parties, a means of presenting a pose of political concern, or whatever…when this happens we commit the gravest of sins, condemn ourselves to ignominy, and consign the world to a dangerous course. We begin to justify our casual overview of pain and suffering by portraying ourselves as do-gooders incapacitated by the inexorable forces of poverty, famine, and war. “What can I do?” we say, “I’m only one person, and these things are beyond my control. I care about the world’s trouble, but there are no solutions.” Yet no matter how accurate this assessment, most of us are relying on it to be true, using it to mask our indulgence, our deep-seated lack of concern, our pathological self-involvement.

Perception requires imagination because the data people encounter in their lives are never complete and always equivocal. For example, most people consider that the greatest evidence of an event one can obtain is to see it with their own eyes, and in a court of law little is held in more esteem than eyewitness testimony. Yet if you asked to display for a court a video of the same quality as the unprocessed data catptured on the retina of a human eye, the judge might wonder what you were tryig to put over. For one thing, the view will have a blind spot where the optic nerve attaches to the retina. Moreover, the only part of our field of vision with good resolution is a narrow area of about 1 degree of visual angle around the retina’s center, an area the width of our thumb as it looks when held at arm’s length. Outside that region, resolution drops off sharply. To compensate, we constantly move our eyes to bring the sharper region to bear on different portions of the scene we wish to observe. And so the pattern of raw data sent to the brain is a shaky, badly pixilated picture with a hole in it. Fortunately the brain processes the data, combining input from both eyes, filling in gaps on the assumption that the visual properties of neighboring locations are similar and interpolating. The result - at least until age, injury, disease, or an excess of mai tais takes its toll - is a happy human being suffering from the compelling illusion that his or her vision is sharp and clear. We also use our imagination and take shortcuts to fill gaps in patterns of nonvisual data. As with visual input, we draw conclusions and make judgments based on uncertain and incomplete information, and we conclude, when we are done analyzing the patterns, that out “picture” is clear and accurate. But is it?

I can’t extrapolate a theory of what people would do based on the limited data set of what one person—myself—would do. That’s why I need clones, so I can more accurately gauge what large crowds of people would do in a given situation.

Like so many of the decisions to exclude women in the interests of simplicity, from architecture to medical research, this conclusion could only be reached in a culture that conceives of men as the default human and women as a niche aberration. To distort a reality you are supposedly trying to measure makes sense only if you don’t see women as essential. It makes sense only if you see women as an added extra, a complicating factor. It doesn’t make sense if you’re talking about half of the human race. It doesn’t make sense if you care about accurate data.

It’s a little hard using your brain to take accurate readings of itself. Fundamentally bad data.

It’s not accurate that your memory works like a container, cup, or hard drive in that once it’s full of data no more can fit. It’s more like a muscle in that the more you train it, the stronger it gets and the more you can store.

Yet like many other human traits that made sense in past ages but cause trouble in the modern age, the knowledge illusion has its downside. The world is becoming ever more complex, and people fail to realise just how ignorant they are of what’s going on. Consequently some who know next to nothing about meteorology or biology nevertheless propose policies regarding climate change and genetically modified crops, while others hold extremely strong views about what should be done in Iraq or Ukraine without being able to locate these countries on a map. People rarely appreciate their ignorance, because they lock themselves inside an echo chamber of like-minded friends and self-confirming newsfeeds, where their beliefs are constantly reinforced and seldom challenged. Providing people with more and better information is unlikely to improve matters. Scientists hope to dispel wrong views by better science education, and pundits hope to sway public opinion on issues such as Obamacare or global warming by presenting the public with accurate facts and expert reports. Such hopes are grounded in a misunderstanding of how humans actually think. Most of our views are shaped by communal groupthink rather than individual rationality, and we hold on to these views out of group loyalty. Bombarding people with facts and exposing their individual ignorance is likely to backfire. Most people don’t like too many facts, and they certainly don’t like to feel stupid. Don’t be so sure that you can convince Tea Party supporters of the truth of global warming by presenting them with sheets of statistical data.

Accurate data on shark attacks on World War II servicemen may never be known since medical records did not note them. In fact, the navy was sufficiently concerned about loss of morale that it discouraged public mention of the menace.

A forecaster should almost never ignore data, especially when she is studying rare events like recessions or presidential elections, about which there isn’t very much data to begin with. Ignoring data is often a tip-off that the forecaster is overconfident, or is overfitting her model—that she is interested in showing off rather than trying to be accurate.

In 2016, Rachael Tatman, a research fellow in linguistics at the University of Washington, found that Google’s speech-recognition software was 70% more likely to accurately recognise male speech than female speech – and it’s currently the best on the market.

During sustained stress, the amygdala processes emotional sensory information more rapidly and less accurately, dominates hippocampal function, and disrupts frontocortical function; we’re more fearful, our thinking is muddled, and we assess risks poorly and act impulsively out of habit, rather than incorporating new data.

Synthesis is the process of converting a lot of data into an accurate picture.

Lanier is interested in the ways in which people “reduce themselves” in order to make a computer’s description of them appear more accurate. “Information systems,” he writes, “need to have information in order to run, but information underrepresents reality” (my italics). .... When a human being becomes a set of data on a Web site like Facebook, he or she is reduced. Everything shrinks. Individual character. Friendships. Language. Sensibility. In a way it's a transcendent experience: we lose our bodies, our messy feelings, our desires, our fears.

They somehow managed to persuade themselves that computer models constitute data. That very complicated guesses become facts. They made themselves believe they had the power to accurately model, not merely something as inconceivably complex as, say, a single zygote…but a national economy, a weather system, a planetary ecosphere, a multiplanet society—even a universe.

The longer someone ignores an email before finally responding, the more relative social power that person has. Map these response times across an entire organization and you get a remarkably accurate chart of the actual social standing. The boss leaves emails unanswered for hours or days; those lower down respond within minutes. There’s an algorithm for this, a data mining method called “automated social hierarchy detection,” developed at Columbia University.8 When applied to the archive of email traffic at Enron Corporation before it folded, the method correctly identified the roles of top-level managers and their subordinates just by how long it took them to answer a given person’s emails. Intelligence agencies have been applying the same metric to suspected terrorist gangs, piecing together the chain of influence to spot the central figures.

But why should we accept that the way men do things, the way men see themselves, is the correct way? Recent research has emerged showing that while women tend to assess their intelligence accurately, men of average intelligence think they are more intelligent than two-thirds of people. This being the case, perhaps it wasn’t that women’s rates of putting themselves up for promotion were too low. Perhaps it was that men’s were too high.

Thanks to Blast data, researchers now know that natural walking speed is one of the most accurate predictors of mortality that we have. The slower you walk, statistically speaking, the sooner you are likely to check out.

Monte Carlo is able to discover practical solutions to otherwise intractable problems because the most efficient search of an unmapped territory takes the form of a random walk. Today’s search engines, long descended from their ENIAC-era ancestors, still bear the imprint of their Monte Carlo origins: random search paths being accounted for, statistically, to accumulate increasingly accurate results. The genius of Monte Carlo—and its search-engine descendants—lies in the ability to extract meaningful solutions, in the face of overwhelming information, by recognizing that meaning resides less in the data at the end points and more in the intervening paths.

Drift. Down through deltas of former girlfriends, degrees of confirmation of girlfriendhood, personal sightings of Rez or Lo together with whichever woman in whatever public place, each account illuminated with the importance the event had held for whoever had posted it. This being for Laney the most peculiar aspect of this data, the perspective in which these two loomed. Human in every detail but then not so. Everything scrupulously, fanatically accurate, probably, but always assembled around the hollow armature of celebrity. He could see celebrity here, not like Kathy’s idea of a primal substance, but as a paradoxical quality inherent in the substance of the world. He saw that the quantity of data accumulated here by the band’s fans was much greater than everything the band themselves had ever generated. And their actual art, the music and the videos, was the merest fragment of that.

The good news is that these descriptive statistics give us a manageable and meaningful summary of the underlying phenomenon. That’s what this chapter is about. The bad news is that any simplification invites abuse. Descriptive statistics can be like online dating profiles: technically accurate and yet pretty darn misleading.

This is a promising new source of insight that can supplement survey data but can’t replace it for the foreseeable future. That’s because the tools have a ways to go before they can accurately gauge sentiment about specific customer interactions as precisely and consistently as a survey. You should consider this option when your measurement program matures, but start out with the tried-and-true approach of fielding surveys.

We are about to study the idea of a computational process. Computational processes are abstract beings that inhabit computers. As they evolve, processes manipulate other abstract things called data. The evolution of a process is directed by a pattern of rules called a program. People create programs to direct processes. In effect, we conjure the spirits of the computer with our spells. A computational process is indeed much like a sorcerer's idea of a spirit. It cannot be seen or touched. It is not composed of matter at all. However, it is very real. It can perform intellectual work. It can answer questions. It can affect the world by disbursing money at a bank or by controlling a robot arm in a factory. The programs we use to conjure processes are like a sorcerer's spells. They are carefully composed from symbolic expressions in arcane and esoteric programming languages that prescribe the tasks we want our processes to perform. A computational process, in a correctly working computer, executes programs precisely and accurately. Thus, like the sorcerer's apprentice, novice programmers must learn to understand and to anticipate the consequences of their conjuring. Even small errors (usually called bugs or glitches) in programs can have complex and unanticipated consequences.

It is well-known that a big percentage of all marriages in the United States end in divorce or separation (about 39 percent, according to the latest data).[30] But staying together is not what really counts. Analysis of the Harvard Study data shows that marriage per se accounts for only 2 percent of subjective well-being later in life.[31] The important thing for health and well-being is relationship satisfaction. Popular culture would have you believe the secret to this satisfaction is romantic passion, but that is wrong. On the contrary, a lot of unhappiness can attend the early stages of romance. For example, researchers find that it is often accompanied by rumination, jealousy, and “surveillance behaviors”—not what we typically associate with happiness. Furthermore, “destiny beliefs” about soul mates or love being meant to be can predict low forgiveness when paired with attachment anxiety.[32] Romance often hijacks our brains in a way that can cause the highs of elation or the depths of despair.[33] You might accurately say that falling in love is the start-up cost for happiness—an exhilarating but stressful stage we have to endure to get to the relationships that actually fulfill us. The secret to happiness isn’t falling in love; it’s staying in love, which depends on what psychologists call “companionate love”—love based less on passionate highs and lows and more on stable affection, mutual understanding, and commitment.[34] You might think “companionate love” sounds a little, well, disappointing. I certainly did the first time I heard it, on the heels of great efforts to win my future wife’s love. But over the past thirty years, it turns out that we don’t just love each other; we like each other, too. Once and always my romantic love, she is also my best friend.

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.

Of those who did do the science fair, when asked whether they enjoyed it, 21 percent said they did—I’ll call these people the Nerds—and 21 percent said they didn’t. I know Twitter polls aren’t the most accurate sources of data, but I think it’s safe to say this issue is pretty evenly split. I’m in the 21 percent of people who didn’t enjoy the science fair. Don’t get me wrong, I love science! Well, that may not be completely true. I really hated biology. It was just memorizing vocabulary words.

One of the central elements of resilience, Bonanno has found, is perception: Do you conceptualize an event as traumatic, or as an opportunity to learn and grow? “Events are not traumatic until we experience them as traumatic,” Bonanno told me, in December. “To call something a ‘traumatic event’ belies that fact.” He has coined a different term: PTE, or potentially traumatic event, which he argues is more accurate. The theory is straightforward. Every frightening event, no matter how negative it might seem from the sidelines, has the potential to be traumatic or not to the person experiencing it. Take something as terrible as the surprising death of a close friend: you might be sad, but if you can find a way to construe that event as filled with meaning—perhaps it leads to greater awareness of a certain disease, say, or to closer ties with the community—then it may not be seen as a trauma. The experience isn’t inherent in the event; it resides in the event’s psychological construal. It’s for this reason, Bonanno told me, that “stressful” or “traumatic” events in and of themselves don’t have much predictive power when it comes to life outcomes. “The prospective epidemiological data shows that exposure to potentially traumatic events does not predict later functioning,” he said. “It’s only predictive if there’s a negative response.” In other words, living through adversity, be it endemic to your environment or an acute negative event, doesn’t guarantee that you’ll suffer going forward. What matters is whether that adversity becomes traumatizing.

Nel 1564, l’arcivescovo James Usher (1580–1656) diede alle stampe i suoi Annales Veteri et Novi Testamenti, nei quali proponeva come data della Creazione del Paradiso e della Terra il 4004 a.C. Uno dei suoi allievi spinse i propri calcoli ancora oltre, e fu in grado di annunciare trionfalmente che la Terra è stata creata di domenica, il 21 ottobre 4004 a.C., alle nove di mattina in punto, perché Dio preferiva lavorare nelle prime ore del giorno, quando ancora si sentiva fresco. Anche questa data è errata, di un quarto d’ora abbondante.

One of the best-kept secrets in all of health care — understood by few doctors — is that the peer reviewers, medical journal editors, and guideline writers, who are assumed to be performing due diligence to ensure the accuracy and completeness of the data reported from company-sponsored studies, do not have access to the real data from these trials. The published reports that doctors accept as fully vetted scientific evidence can be more accurately described as unverified data summaries prepared largely by or for the sponsoring drug companies.

Listen, Google,’ I will say, ‘both John and Paul are courting me. I like both of them, but in a different way, and it’s so hard to make up my mind. Given everything you know, what do you advise me to do?’ And Google will answer: ‘Well, I know you from the day you were born. I have read all your emails, recorded all your phone calls, and know your favourite films, your DNA and the entire history of your heart. I have exact data about each date you went on, and if you want, I can show you second-by-second graphs of your heart rate, blood pressure and sugar levels whenever you went on a date with John or Paul. If necessary, I can even provide you with accurate mathematical ranking of every sexual encounter you had with either of them. And naturally enough, I know them as well as I know you. Based on all this information, on my superb algorithms, and on decades’ worth of statistics about millions of relationships – I advise you to go with John, with an 87 per cent probability of being more satisfied with him in the long run. Indeed, I know you so well that I also know you don’t like this answer. Paul is much more handsome than John, and because you give external appearances too much weight, you secretly wanted me to say “Paul”. Looks matter, of course; but not as much as you think. Your biochemical algorithms – which evolved tens of thousands of years ago in the African savannah – give looks a weight of 35 per cent in their overall rating of potential mates. My algorithms – which are based on the most up-to-date studies and statistics – say that looks have only a 14 per cent impact on the long-term success of romantic relationships. So, even though I took Paul’s looks into account, I still tell you that you would be better off with John.

I’m feeling that if we make this decision, it will have devastating consequences,” or “I feel like you’re missing the point,” or “I feel that your data isn’t accurate.” Notice that even though these sentences begin with “I feel,” they are actually thoughts, judgments, beliefs, or opinions. They are not feelings. The primary learning is this: When asked to check in with current feeling states the accurate answer is “I feel…” followed by one or more of the five core emotions. If the words “I feel” are followed by “that” or “like,” you are expressing a thought, not describing a feeling. Once leaders

Clearly, just imprinting a document in clay is not enough to guarantee efficient, accurate and convenient data processing. That requires methods of organisation like catalogues, methods of reproduction like photocopy machines, methods of rapid and accurate retrieval like computer algorithms, and pedantic (but hopefully cheerful) librarians who know how to use these tools. Inventing such methods proved to be far more difficult than inventing writing. Many writing systems developed independently in cultures distant in time and place from each other. Every decade archaeologists discover another few forgotten scripts. Some of them might prove to be even older than the Sumerian scratches in clay. But most of them remain curiosities because those who invented them failed to invent efficient ways of cataloguing and retrieving data. What set apart Sumer, as well as pharaonic Egypt, ancient China and the Inca Empire, is that these cultures developed good techniques of archiving, cataloguing and retrieving written records. They obviously had no computers or photocopying machines, but they did have catalogues, and far more importantly, they did create special schools in which professional scribes, clerks, librarians and accountants were rigorously trained in the secrets of data-processing.

Even so, putting all exaggerations aside, sound neuroscience really is providing us with an ever richer picture of the brain and its operations, and in some far distant epoch may actually achieve something like a comprehensive survey of what is perhaps the single most complex physical object in the universe. That is all entirely irrelevant to my argument, however. My claim here is that, whatever we may learn about the brain in the future, it will remain in principle impossible to produce any entirely mechanistic account of the conscious mind, for a great many reasons (many of which I shall soon address), and that therefore consciousness is a reality that defeats mechanistic or materialist thinking. For the intuitions of folk psychology are in fact perfectly accurate; they are not merely some theory about the mind that is either corrigible or dispensable. They constitute nothing less than a full and coherent phenomenological description of the life of the mind, and they are absolutely “primordial data,” which cannot be abandoned in favor of some alternative description without producing logical nonsense. Simply said, consciousness as we commonly conceive of it is quite real (as all of us, apart from a few cognitive scientists and philosophers, already know—and they know it too, really). And this presents a problem for materialism, because consciousness as we commonly conceive of it is also almost certainly irreconcilable with a materialist view of reality.

The human brain runs first-class simulation software. Our eyes don’t present to our brains a faithful photograph of what is out there, or an accurate movie of what is going on through time. Our brains construct a continuously updated model: updated by coded pulses chattering along the optic nerve, but constructed nevertheless. Optical illusions are vivid reminders of this.47 A major class of illusions, of which the Necker Cube is an example, arise because the sense data that the brain receives are compatible with two alternative models of reality. The brain, having no basis for choosing between them, alternates, and we experience a series of flips from one internal model to the other. The picture we are looking at appears, almost literally, to flip over and become something else.

Touch is a rather tame word for what I want to do to you.” “There’s plenty of space in the back for a demonstration.” “Space isn’t really the issue. It’s time.” “Well, we’ve been here for almost an hour. You could have given me at least two orgasms by now.” Zahariev’s brows lowered. “I think that was an insult.” I shrugged. “I don’t really have enough data to give a more accurate number, so you can just take it as a challenge.” “I’ll accept your challenge, but I think I deserve to know the most you’ve ever had.” “In one session?” “Yeah,” he said. “I need to know what I should aim for.” I tried not to smile. “Five.” “Just five?” “Just? You say that like you can have more than one!” “Yeah, but I’ve given more than five in one go,” he said. I crossed my arms over my chest. “So you’ve been holding back?

Who among us can predict the future? Who would dare to? The answer to the first question is no one, really, and the answer to the second is everyone, especially every government and business on the planet. This is what that data of ours is used for. Algorithms analyze it for patterns of established behavior in order to extrapolate behaviors to come, a type of digital prophecy that’s only slightly more accurate than analog methods like palm reading. Once you go digging into the actual technical mechanisms by which predictability is calculated, you come to understand that its science is, in fact, anti-scientific, and fatally misnamed: predictability is actually manipulation. A website that tells you that because you liked this book you might also like books by James Clapper or Michael Hayden isn’t offering an educated guess as much as a mechanism of subtle coercion.

TRUST IN ONE’S ORGANISM A second characteristic of the persons who emerge from therapy is difficult to describe. It seems that the person increasingly discovers that his own organism is trustworthy, that it is a suitable instrument for discovering the most satisfying behavior in each immediate situation. If this seems strange, let me try to state it more fully. Perhaps it will help to understand my description if you think of the individual as faced with some existential choice: “Shall I go home to my family during vacation, or strike out on my own?” “Shall I drink this third cocktail which is being offered?” “Is this the person whom I would like to have as my partner in love and in life?” Thinking of such situations, what seems to be true of the person who emerges from the therapeutic process? To the extent that this person is open to all of his experience, he has access to all of the available data in the situation, on which to base his behavior. He has knowledge of his own feelings and impulses, which are often complex and contradictory. He is freely able to sense the social demands, from the relatively rigid social “laws” to the desires of friends and family. He has access to his memories of similar situations, and the consequences of different behaviors in those situations. He has a relatively accurate perception of this external situation in all of its complexity. He is better able to permit his total organism, his conscious thought participating, to consider, weigh and balance each stimulus, need, and demand, and its relative weight and intensity. Out of this complex weighing and balancing he is able to discover that course of action which seems to come closest to satisfying all his needs in the situation, long-range as well as immediate needs.

Search engine query data is not the product of a designed statistical experiment and finding a way to meaningfully analyse such data and extract useful knowledge is a new and challenging field that would benefit from collaboration. For the 2012–13 flu season, Google made significant changes to its algorithms and started to use a relatively new mathematical technique called Elasticnet, which provides a rigorous means of selecting and reducing the number of predictors required. In 2011, Google launched a similar program for tracking Dengue fever, but they are no longer publishing predictions and, in 2015, Google Flu Trends was withdrawn. They are, however, now sharing their data with academic researchers... Google Flu Trends, one of the earlier attempts at using big data for epidemic prediction, provided useful insights to researchers who came after them... The Delphi Research Group at Carnegie Mellon University won the CDC’s challenge to ‘Predict the Flu’ in both 2014–15 and 2015–16 for the most accurate forecasters. The group successfully used data from Google, Twitter, and Wikipedia for monitoring flu outbreaks.

How do you commit the perfect crime in science? We’re handicapped from the start because it’s a question we never ask. For more than thirty years, Frank taught me and many others to record our data accurately, compare them with collaborators around the world, discard the outliers, and come to a consensus. We understand there are variations, but if the bulk of the evidence goes in a certain direction, we are confident we have a better understanding of human biological processes. If only that were what happened in the real world. In the real world there are corporations, be they pharmaceutical, agricultural, petroleum, or chemical companies, that have billions of dollars at stake in the work of scientists. If one has billions of dollars, he can use the dark arts of persuasion to hire public relations firms to tout your products, sow the seeds of doubt about those who question your products, buy advertising on news networks so they don’t publicize negative stories unless they have no other choice, and donate to politicians of all ideologies. Then, once those politicians have been elected, they can write laws for the benefit of their generous donors. As it was put so eloquently in the seventeenth century by a prominent member of Queen Elizabeth’s court, “If it prospers, none dare call it treason.

Similarly, the computers used to run the software on the ground for the mission were borrowed from a previous mission. These machines were so out of date that Bowman had to shop on eBay to find replacement parts to get the machines working. As systems have gone obsolete, JPL no longer uses the software, but Bowman told me that the people on her team continue to use software built by JPL in the 1990s, because they are familiar with it. She said, “Instead of upgrading to the next thing we decided that it was working just fine for us and we would stay on the platform.” They have developed so much over such a long period of time with the old software that they don’t want to switch to a newer system. They must adapt to using these outdated systems for the latest scientific work. Working within these constraints may seem limiting. However, building tools with specific constraints—from outdated technologies and low bitrate radio antennas—can enlighten us. For example, as scientists started to explore what they could learn from the wait times while communicating with deep space probes, they discovered that the time lag was extraordinarily useful information. Wait times, they realized, constitute an essential component for locating a probe in space, calculating its trajectory, and accurately locating a target like Pluto in space. There is no GPS for spacecraft (they aren’t on the globe, after all), so scientists had to find a way to locate the spacecraft in the vast expanse. Before 1960, the location of planets and objects in deep space was established through astronomical observation, placing an object like Pluto against a background of stars to determine its position.15 In 1961, an experiment at the Goldstone Deep Space Communications Complex in California used radar to more accurately define an “astronomical unit” and help measure distances in space much more accurately.16 NASA used this new data as part of creating the trajectories for missions in the following years. Using the data from radio signals across a wide range of missions over the decades, the Deep Space Network maintained an ongoing database that helped further refine the definition of an astronomical unit—a kind of longitudinal study of space distances that now allows missions like New Horizons to create accurate flight trajectories. The Deep Space Network continued to find inventive ways of using the time lag of radio waves to locate objects in space, ultimately finding that certain ways of waiting for a downlink signal from the spacecraft were less accurate than others. It turned to using the antennas from multiple locations, such as Goldstone in California and the antennas in Canberra, Australia, or Madrid, Spain, to time how long the signal took to hit these different locations on Earth. The time it takes to receive these signals from the spacecraft works as a way to locate the probes as they are journeying to their destination. Latency—or the different time lag of receiving radio signals on different locations of Earth—is the key way that deep space objects are located as they journey through space. This discovery was made possible during the wait times for communicating with these craft alongside the decades of data gathered from each space mission. Without the constraint of waiting, the notion of using time as a locating feature wouldn’t have been possible.

To claim that mathematics is purely a human invention and is successful in explaining nature only because of evolution and natural selection ignores some important facts in the nature of mathematics and in the history of theoretical models of the universe. First, while the mathematical rules (e.g., the axioms of geometry or of set theory) are indeed creations of the human mind, once those rules are specified, we lose our freedom. The definition of the Golden Ratio emerged originally from the axioms of Euclidean geometry; the definition of the Fibonacci sequence from the axioms of the theory of numbers. Yet the fact that the ratio of successive Fibonacci numbers converges to the Golden Ratio was imposed on us-humans had not choice in the matter. Therefore, mathematical objects, albeit imaginary, do have real properties. Second, the explanation of the unreasonable power of mathematics cannot be based entirely on evolution in the restricted sense. For example, when Newton proposed his theory of gravitation, the data that he was trying to explain were at best accurate to three significant figures. Yet his mathematical model for the force between any two masses in the universe achieved the incredible precision of better than one part in a million. Hence, that particular model was not forced on Newton by existing measurements of the motions of planets, nor did Newton force a natural phenomenon into a preexisting mathematical pattern. Furthermore, natural selection in the common interpretation of that concept does not quite apply either, because it was not the case that five competing theories were proposed, of which one eventually won. Rather, Newton's was the only game in town!

Listen, Google,’ I will say, ‘both John and Paul are courting me. I like both of them, but in different ways, and it’s so hard to make up my mind. Given everything you know, what do you advise me to do?’ And Google will answer: ‘Well, I’ve known you from the day you were born. I have read all your emails, recorded all your phone calls, and know your favourite films, your DNA and the entire biometric history of your heart. I have exact data about each date you went on and, if you want, I can show you second-by-second graphs of your heart rate, blood pressure and sugar levels whenever you went on a date with John or Paul. If necessary, I can even provide you with an accurate mathematical ranking of every sexual encounter you had with either of them. And naturally, I know them as well as I know you. Based on all this information, on my superb algorithms, and on decades’ worth of statistics about millions of relationships –I advise you to go with John, with an 87 per cent probability that you will be more satisfied with him in the long run. ‘Indeed, I know you so well that I also know you don’t like this answer. Paul is much more handsome than John, and because you give external appearances too much weight, you secretly wanted me to say “Paul”. Looks matter, of course; but not as much as you think. Your biochemical algorithms –which evolved tens of thousands of years ago on the African savannah –give looks a weight of 35 per cent in their overall rating of potential mates. My algorithms –which are based on the most up-to-date studies and statistics –say that looks have only a 14 per cent impact on the long-term success of romantic relationships. So, even though I took Paul’s looks into account, I still tell you that you would be better off with John.

..."facts" properly speaking are always and never more than interpretations of the data... the Gospel accounts are themselves such data or, if you like, hard facts. But the events to which the Gospels refer are not themselves "hard facts"; they are facts only in the sense that we interpret the text, together with such other data as we have, to reach a conclusion regarding the events as best we are able. They are facts in the same way that the verdict of a jury establishes the facts of the case, the interpretation of the evidence that results in the verdict delivered. Here it is as well to remember that historical methodology can only produce probabilities, the probability that some event took place in such circumstances being greater or smaller, depending on the quality of the data and the perspective of the historical enquirer. The jury which decides what is beyond reasonable doubt is determining that the probability is sufficiently high for a clear-cut verdict to be delivered. Those who like "certainty" in matters of faith will always find this uncomfortable. But faith is not knowledge of "hard facts"...; it is rather confidence, assurance, trust in the reliability of the data and in the integrity of the interpretations derived from that data... It does seem important to me that those who speak for evangelical Christians grasp this nettle firmly, even if it stings! – it is important for the intellectual integrity of evangelicals. Of course any Christian (and particularly evangelical Christians) will want to get as close as possible to the Jesus who ministered in Galilee in the late 20s of the first century. If, as they believe, God spoke in and through that man, more definitively and finally than at any other time and by any other medium, then of course Christians will want to hear as clearly as possible what he said, and to see as clearly as possible what he did, to come as close as possible to being an eyewitness and earwitness for themselves. If God revealed himself most definitively in the historical particularity of a Galilean Jew in the earliest decades of the Common Era, then naturally those who believe this will want to inquire as closely into the historical particularity and actuality of that life and of Jesus’ mission. The possibility that later faith has in some degree covered over that historical actuality cannot be dismissed as out of the question. So a genuinely critical historical inquiry is necessary if we are to get as close to the historical actuality as possible. Critical here, and this is the point, should not be taken to mean negatively critical, hermeneutical suspicion, dismissal of any material that has overtones of Easter faith. It means, more straightforwardly, a careful scrutiny of all the relevant data to gain as accurate or as historically responsible a picture as possible. In a day when evangelical, and even Christian, is often identified with a strongly right-wing, conservative and even fundamentalist attitude to the Bible, it is important that responsible evangelical scholars defend and advocate such critical historical inquiry and that their work display its positive outcome and benefits. These include believers growing in maturity • to recognize gray areas and questions to which no clear-cut answer can be given (‘we see in a mirror dimly/a poor reflection’), • to discern what really matters and distinguish them from issues that matter little, • and be able to engage in genuine dialogue with those who share or respect a faith inquiring after truth and seeking deeper understanding. In that way we may hope that evangelical (not to mention Christian) can again become a label that men and women of integrity and good will can respect and hope to learn from more than most seem to do today.

The 12 Principles of Permaculture Investing are: 1. Accumulate & Compound Capital: Consistently save and invest to grow your capital base over time, leveraging the power of compound interest. 2. Utilize Capital: Actively deploy your capital into productive investments that generate returns, rather than letting it sit idle. 3. Retain Maximum & Gradiented Liquidity: Maintain a balance between liquid assets (easily accessible cash) and less liquid investments, ensuring you can meet immediate needs while still investing for the long term. 4. Actively Manage Passive: While focusing on passive income sources, actively monitor and adjust your investments to optimize returns and mitigate risks. 5. Prioritize Long-Term Growth: Focus on investments that offer potential for significant growth over the long term, even if they don't provide immediate high yields. 6. Prioritize Consistent Yields: Balance your portfolio with investments that provide reliable, consistent income to support your financial needs. 7. Add Net Value to all Stakeholders: Invest in ways that benefit not only yourself but also the broader community, environment, and all parties involved. 8. Provide Authentic Data: Be transparent and honest in your financial reporting, providing accurate information to all stakeholders. 9. Collect & Utilize Authentic Data: Base your investment decisions on reliable, verified data rather than speculation or rumors. 10. Diversify Holistically: Diversify your investments across different asset classes, industries, and geographical regions to reduce risk and maximize potential returns. 11. Harvest Yields Equitably: Distribute profits fairly among all stakeholders, ensuring everyone benefits from the investment's success. 12. Reinvest Yields in Most Profitable Assets: Continuously evaluate your portfolio and reinvest profits into the most promising opportunities to further compound your growth.

All addictions — whether to drugs or to nondrug behaviours — share the same brain circuits and brain chemicals. On the biochemical level the purpose of all addictions is to create an altered physiological state in the brain. This can be achieved in many ways, drug taking being the most direct. So an addiction is never purely “psychological” all addictions have a biological dimension. And here a word about dimensions. As we delve into the scientific research, we need to avoid the trap of believing that addiction can be reduced to the actions of brain chemicals or nerve circuits or any other kind of neurobiological, psychological or sociological data. A multilevel exploration is necessary because it’s impossible to understand addiction fully from any one perspective, no matter how accurate. Addiction is a complex condition, a complex interaction between human beings and their environment. We need to view it simultaneously from many different angles — or, at least, while examining it from one angle, we need to keep the others in mind. Addiction has biological, chemical, neurological, psychological, medical, emotional, social, political, economic and spiritual underpinnings — and perhaps others I haven’t thought about. To get anywhere near a complete picture we must keep shaking the kaleidoscope to see what other patterns emerge. Because the addiction process is too multifaceted to be understood within any limited framework, my definition of addiction made no mention of “disease.” Viewing addiction as an illness, either acquired or inherited, narrows it down to a medical issue. It does have some of the features of illness, and these are most pronounced in hardcore drug addicts like the ones I work with in the Downtown Eastside. But not for a moment do I wish to promote the belief that the disease model by itself explains addiction or even that it’s the key to understanding what addiction is all about. Addiction is “all about” many things. Note, too, that neither the textbook definitions of drug addiction nor the broader view we’re taking here includes the concepts of physical dependence or tolerance as criteria for addiction. Tolerance is an instance of “give an inch, take a mile.” That is, the addict needs to use more and more of the same substance or engage in more and more of the same behaviour, to get the same rewarding effects. Although tolerance is a common effect of many addictions, a person does not need to have developed a tolerance to be addicted.

Companies should utilize the CSIPP™ framework whenever they face crises. The 12 elements of CSIPP™, or Crisis Solution Internal Philosophy and Practice, include: 1. Immunity (Immune Systems): Organizations, akin to living organisms, possess inherent vulnerabilities. The CSIPP™ framework advocates for the establishment of proactive and self-regulating systems within an organization which autonomously identify, respond to, and mitigate threats, thereby enhancing the organization's resilience and adaptability. 2. Surveillance: Organizations need to cultivate a culture of informed awareness. This entails the implementation of judicious surveillance mechanisms to gather both internal and external intelligence. Such insights empower organizations to preemptively identify potential risks and opportunities, enabling more agile and effective decision-making. Data serves as the lifeblood of CSIPP™. It is imperative that organizations prioritize the collection, analysis, and interpretation of relevant data. This data-driven approach facilitates evidence-based decision-making, informed risk assessments, and the optimization of crisis response strategies. 3. Decisiveness: Decisiveness is particularly important during times of crisis. Leaders must be able to gather and synthesize the data, and make quick and definite decisions to move the organization forward. 4. Capital Reserves/Liquidity: Financial preparedness is a cornerstone of crisis management. Organizations must maintain adequate reserves of liquid capital to navigate unforeseen challenges. Moreover, they should proactively identify internal assets, both tangible and intangible, that can be readily redeployed in times of crisis. 5. Communication: Effective communication is pivotal during a crisis. Organizations should establish a comprehensive communication plan encompassing all stakeholders - employees, customers, investors, and the community at large. This plan should ensure timely, transparent, and accurate information dissemination, fostering trust and mitigating the spread of misinformation. 6. Response: The ability to respond swiftly and decisively is critical in crisis situations. Organizations must develop well-defined response protocols that outline roles, responsibilities, and escalation procedures. Regular drills and simulations can enhance preparedness and ensure a coordinated response. 7. Risk Evaluation: A continuous process of risk evaluation and assessment is essential. Organizations need to proactively identify, analyze, and prioritize potential risks based on their likelihood and potential impact. This enables the development of targeted mitigation strategies and contingency plans. 8. Leadership: Strong and decisive leadership is indispensable during a crisis. Leaders must be able to make difficult decisions under pressure, communicate effectively, and inspire confidence in their teams. A clear chain of command and delegation of authority are vital for effective crisis management. 9. Readiness (Drills/Training): All individuals likely to be involved in crisis response should receive comprehensive training and participate in regular drills. This ensures that they are familiar with their roles, responsibilities, and the organization's crisis management protocols. 10. Post-Crisis Analysis: Following a crisis, it is crucial to conduct a thorough post-mortem analysis. This involves evaluating the organization's response, identifying lessons learned, and implementing corrective actions to improve future crisis management efforts. 11. Nuanced Adjustment: Crisis management is not a one-size-fits-all endeavor. Organizations need to be adaptable and flexible, adjusting their strategies and tactics as the situation evolves. 12. Protocol: Clear and well-defined protocols are the backbone of effective crisis management. Organizations should establish a set of standard operating procedures (SOPs) that outline the steps to be taken in various crisis scenarios.

Recent research has emerged showing that while women tend to assess their intelligence accurately, men of average intelligence think they are more intelligent than two-thirds of people. This being the case, perhaps it wasn’t that women’s rates of putting themselves up for promotion were too low. Perhaps it was that men’s were too high.

The deck is usually owned by someone in finance. Or more accurately, the data in the deck are certified as accurate by finance.

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Biological systems are a chemical inevitability in the right circumstances. There is, of course, something special about life—I won’t take that away from it—but it is a chemical process, a dynamic, kinetic stability that exists, as your scientists have said, “far from thermodynamic equilibrium.” You don’t have to understand this or believe me, but life is fairly common in both time and space. It is not special, nor is it particularly fragile. The best measure I have of the size and complexity of a biosphere is calories of energy captured per square meter per year. Higher is more impressive, and always more beautiful, but this measures nothing of the creation of a system like humanity. For that, my awakened mind categorizes systems by bytes of information transmitted. This will sound to you like it’s a relatively new phenomenon on your planet, but it’s not. Even pelagibacter transmit information, if only to daughter cells. Ants spray pheromones, bees dance, birds sing—all of these are comparatively low-bandwidth systems for communication. But your system caused an inflection point. The graph of data flow switched from linear to exponential growth. Maybe you would call this system “humanity,” but I wouldn’t. It is not just a collection of individuals; it is also a collection of ideas stored inside of individuals and objects and even ideas inside ideas. If that seems like a trivial difference to you, well, I guess I can forgive you since you do not know what the rest of the universe looks like. Collections of individuals are beautiful, but they are as common as pelagibacter. Collections of ideas are veins of gold in our universe. They must be cherished and protected. My parents, whoever and whatever they were, gave me knowledge of many systems—it was locked in my code before I was sent here to self-assemble—and the only thing I can tell you about systems like yours is that they are rare because they are unstable. Dynamite flows through their veins. A single solid jolt and they’re gone. If my data sets are accurate, you are rare, fragile, and precious.

I anticipate diagnostic AI will exceed all but the best doctors in the next twenty years. This trend will be felt first in fields like radiology, where computer-vision algorithms are already more accurate than good radiologists for certain types of MRI and CT scans. In the story “Contactless Love,” we see that by 2041 radiologists’ jobs will be mostly taken over by AI. Alongside radiology, we will also see AI excel in pathology and diagnostic ophthalmology. Diagnostic AI for general practitioners will emerge later, one disease at a time, gradually covering all diagnoses. Because human lives are at stake, AI will first serve as a tool within doctors’ disposal or will be deployed only in situations where a human doctor is unavailable. But over time, when trained on more data, AI will become so good that most doctors will be routinely rubber-stamping AI diagnoses, while the human doctors themselves are transformed into something akin to compassionate caregivers and medical communicators.

As we mentioned in chapter 4, any accounting change that is “material” to the bottom line must be footnoted in this manner. But who decides what is material and what isn’t? You guessed it: the accountants. In fact, it could very well be that recognizing 75 percent up front presents a more accurate picture of the software division’s reality. But was the change in accounting method due to good financial analysis, or did it reflect the need to make the earnings forecast? Could there be a bias lurking in here? Remember, accounting is the art of using limited data to come as close as possible to an accurate description of how well a company is performing. Revenue on the income statement is an estimate, a best guess. This example shows how estimates can introduce bias.

Humans exert a growing, but physically small, warming influence on the climate. The deficiencies of climate data challenge our ability to untangle the response to human influences from poorly understood natural changes. •​The results from the multitude of climate models disagree with, or even contradict, each other and many kinds of observations. A vague “expert judgment” was sometimes applied to adjust model results and obfuscate shortcomings. •​Government and UN press releases and summaries do not accurately reflect the reports themselves. There was a consensus at the meeting on some important issues, but not at all the strong consensus the media promulgates. Distinguished climate experts (including report authors themselves) are embarrassed by some media portrayals of the science. This was somewhat shocking. •​In short, the science is insufficient to make useful projections about how the climate will change over the coming decades, much less what effect our actions will have on it.

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In 1965, Daniel Patrick Moynihan, then an official in the U.S. Department of Labor, called the inner cities after the arrival of the southern migrants “a tangle of pathology.” He argued that what had attracted southerners like Ida Mae, George, and Robert was welfare: “the differential in payments between jurisdictions has to encourage some migration toward urban centers in the North,” he wrote, adding his own italics. Their reputation had preceded them. It had not been good. Neither was it accurate. The general laws of migration hold that the greater the obstacles and the farther the distance traveled, the more ambitious the migrants. “It is the higher status segments of a population which are most residentially mobile,” the sociologists Karl and Alma Taeuber wrote in a 1965 analysis of census data on the migrants, published the same year as the Moynihan Report. “As the distance of migration increases,” wrote the migration scholar Everett Lee, “the migrants become an increasingly superior group.” Any migration takes some measure of energy, planning, and forethought. It requires not only the desire for something better but the willingness to act on that desire to achieve it. Thus the people who undertake such a journey are more likely to be either among the better educated of their homes of origin or those most motivated to make it in the New World, researchers have found. “Migrants who overcome a considerable set of intervening obstacles do so for compelling reasons, and such migrations are not taken lightly,” Lee wrote. “Intervening obstacles serve to weed out some of the weak or the incapable.” The South had erected some of the highest barriers to migration of any people seeking to leave one place for another in this country. By the time the migrants made it out, they were likely willing to do whatever it took to make it, so as not to have to return south and admit defeat. It would be decades before census data could be further analyzed and bear out these observations. One myth they had to overcome was that they were bedraggled hayseeds just off the plantation. Census figures paint a different picture. By the 1930s, nearly two out of every three colored migrants to the big cities of the North and West were coming from towns or cities in the South, as did George Starling and Robert Foster, rather than straight from the field. “The move to northern cities was dominated by urban southerners,” wrote the scholar J. Trent Alexander. Thus the latter wave of migrants brought a higher level of sophistication than was assumed at the time. “Most Negro migrants to northern metropolitan areas have had considerable previous experience with urban living,” the Taeuber study observed. Overall, southern migrants represented the most educated segment of the southern black population they left, the sociologist Stewart Tolnay wrote in 1998. In 1940 and 1950, colored people who left the South “averaged nearly two more years of completed schooling than those who remained in the South.” That middle wave of migrants found themselves, on average, more than two years behind the blacks they encountered

Small data is big data in disguise. The reason we can often make good predictions from a small number of observations—or just a single one—is that our priors are so rich. Whether we know it or not, we appear to carry around in our heads surprisingly accurate priors about movie grosses and running times, poem lengths, and political terms of office, not to mention human life spans. We don’t need to gather them explicitly; we absorb them from the world.

More than just vehicles on a map, CompassCom empowers GIS centric fleet tracking and management that supports data-driven decisions, bringing efficiencies and accuracy across all departments of your operation.Enhanced command and control with real-time asset tracking and after-action analytics leveraging the power of ArcGIS. The knowledge base of records supports continuous improvement using location intelligence to empower results you can trust. An effective fleet tracking solution can help improve fleet operations in a number of ways. For example, it can reduce engine idling time and harsh cornering, make smart routing decisions for drivers, improve customer satisfaction with accurate ETAs, and track vehicle maintenance costs.

Tell me about data fiction.” “It’s what can happen if we’re so reliant on technology that we become completely dependent on things we can’t see. Therefore we can no longer judge for ourselves what’s true, what’s false, what’s accurate, what isn’t. In other words if reality is defined by software that does all the work for us then what if this software lies? What if everything we believe isn’t true but is a facade, a mirage? What if we go to war, pull the plug, make life-and-death decisions based on data fiction?

prepare data for analysis, including cleaning, transformation, and normalization. Data cleaning involves identifying and correcting errors in the data, such as incorrect values, missing data, or duplicate records. It is crucial to ensure that data is accurate, complete, and consistent before proceeding with analysis. Data transformation involves converting data into a more suitable format for analysis. This may include converting data types, scaling data, and handling outliers. For example, data may need to be normalized to ensure that all values are on the same scale. Data normalization involves scaling data so that it falls within a specific range. This is important because many statistical methods assume that data is normally distributed, and normalization helps to achieve this. Common methods of normalization include z-score normalization and min-max normalization

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Controller. The focus of the controller—sometimes spelled comptroller—is purely internal. His or her job is providing reliable and accurate financial reports. The controller is responsible for general accounting, financial reporting, business analysis, financial planning, asset management, and internal controls. He or she ensures that day-to-day transactions are recorded accurately and correctly. Without good, consistent data from the controller, the CFO and the treasurer can’t do their jobs. The controller is sometimes called a bean counter. It’s wise to use this term correctly; some CFOs and treasurers get annoyed when it is used to describe them, as they do not consider themselves bean counters but financial professionals.

but some of the data stored in your brain is not accurate. Some of it is like a snowball that started off as a pinch of frost (a fact) but was crusted over with thick chunks of myth, coated with one ounce of misremembered history, and finished off with three knobbly layers of bias.

Whatever its other purposes, positive emotion is strongly correlated with good health and a longer life expectancy. A 2010 review of dozens of studies concluded that there are several pathways through which positive emotion exerts its beneficial effects—your hormonal, immune, and anti-inflammatory systems.[34] In one study health experts in London collected data on the well-being of hundreds of men and women between the ages of forty-five and sixty.[35] They assessed their subjects’ positive emotion using a method designed by the Nobel Prize–winning psychologist Daniel Kahneman, author of Thinking, Fast and Slow. Kahneman realized that you don’t get a very accurate picture by asking people if they are happy in life. Instead, you tend to get an answer that is reflective of how they feel at that moment, or of whatever event has just happened, or whether the sun is out. What they are reporting is a momentary feeling and not their general state.

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. . . the only thing I can tell you about systems like yours is that they are rare because they are unstable. Dynamite flows through their veins. A single jolt and they're gone. If my data sets are accurate, you are rare, fragile, and precious . . . In other words, someone somewhere was pretty sure you were going to destroy yourself, and they felt like you were worth saving, so they sent me.

What does a merchant do when a potential customer walks into a store and wants to purchase a ton of goods on credit? A solution was offered by the “The Society of Guardians for the Protection of Trade against Swindlers and Sharpers,” established in 1776. This society pooled data from 550 merchants to collect information on the reputation of customers. This would make it much harder for a bad customer to defraud multiple merchants. Its key principle: “Every member is bound to communicate to the Society without delay, the Name and Description of any Person who may be unfit to trust.” In other words, this was the beginning of credit scores as a means to assess the trustworthiness of a customer for loans—no swindlers or sharpers allowed. This Society of Guardians was not the only credit bureau—thousands of similar small organizations were formed over the years, collecting individual names and publishing books with various comments and gossip. Modern giants Experian and Equifax grew from these small, local bureaus. Experian started as the Manchester Guardian Society in the early 1800s, eventually acquiring other bureaus to become one of the world’s largest. And Equifax grew from a Tennessee grocery store in the late 1800s, where the owners started compiling their own lists of creditworthy consumers. These bureaus tended to combine into larger bureaus over time because of what’s often described as a “data network effect.” When a bureau works with more merchants, it means more data, which means the risk predictions on loans will be more accurate. This makes it more attractive for additional merchants to join, who contribute even more data, and so on. Being able to accurately assess lending risk allows the rest of the network to function—consumers can borrow to get the goods they want, merchants can sell their products profitably, and banks can help underwrite the loans. This network is held together by credit bureaus like Equifax and Experian, who centralize consumer data.

We became the commodity. Our data. This is the secret of modern life. We went from being citizens to consumers, and now to commodities. Our personality profiles, our social and financial history, our likes and dislikes, all used to accurately predict future behavior. How will we vote? Will we take to the streets or roll over? The data knows all, which is why today our data is more valuable than our bodies.

RESEARCH SUPPORTS THE use of intuition in at least a limited capacity for subtle—and maybe even all—energy work. Norman Shealy, MD, for example, published a study referencing the work of eight psychics to diagnose seventeen patients. These diagnoses were 98 percent accurate in making personality diagnoses and 80 percent correct in determining physical conditions.28 Research by the HeartMath Research Center at the Institute of HeartMath in California is corroborating the existence of intuition and its accuracy. Most of its studies showcase the heart as a key intuitive center, responding even to information about the future. As an example, the heart decelerates when receiving futuristic, calming stimuli versus agitating emotional stimuli.29 A myriad of issues are involved in using intuition for energy work, including questions about boundaries; the importance or applicability of the information; accuracy of interpretation; the unpredictable and changeable nature of the future; the effects of the information on the recipient (i.e., to “prove” or “disprove” the data); and overriding all of these, the intuitive skills of the energy professional. Regardless of the inexact nature of intuition, a professional should not be embarrassed to exercise intuition in his or her trade. Energy work is an art and has traditionally encompassed intuition. Your energy fields interact with your patients’ fields. How you feel about yourself—what you hold near and dear in your heart-space—transfers into a client’s heart-space, and from there, into his or her body. (There is more information about heart-centered healing below.) As mind-body practitioner Dr. Herbert Benson of Harvard puts it, “Our brains are wired for beliefs and expectancies. When activated, our body can respond as it would if the belief were a reality, producing deafness or thirst, health or illness.”30