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.

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.

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.

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.

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.

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.

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

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.

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.

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.

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.

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.

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.

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.

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It’s easy for you to tell what it’s a photo of, but to program a function that inputs nothing but the colors of all the pixels of an image and outputs an accurate caption such as “A group of young people playing a game of frisbee” had eluded all the world’s AI researchers for decades. Yet a team at Google led by Ilya Sutskever did precisely that in 2014. Input a different set of pixel colors, and it replies “A herd of elephants walking across a dry grass field,” again correctly. How did they do it? Deep Blue–style, by programming handcrafted algorithms for detecting frisbees, faces and the like? No, by creating a relatively simple neural network with no knowledge whatsoever about the physical world or its contents, and then letting it learn by exposing it to massive amounts of data. AI visionary Jeff Hawkins wrote in 2004 that “no computer can…see as well as a mouse,” but those days are now long gone.

Determine and fill in the missing value manually. In general, this approach is the most accurate but it is also time-consuming and often is not feasible in a large dataset with many missing

If, as I believe, the conceptual structures we construct today are too complicated to be accurately specified in advance, and too complex to be built faultlessly, then we must take a radically different approach. Let us turn to nature and study complexity in living things, instead of just the dead works of man. Here we find constructs whose complexities thrill us with awe. The brain alone is intricate beyond mapping, powerful beyond imitation, rich in diversity, self-protecting, and self-renewing. The secret is that it is grown, not built. So it must be with our software systems. Some years ago Harlan Mills proposed that any software system should be grown by incremental development.[11] That is, the system should first be made to run, even though it does nothing useful except call the proper set of dummy subprograms. Then, bit by bit it is fleshed out, with the subprograms in turn being developed into actions or calls to empty stubs in the level below. I have seen the most dramatic results since I began urging this technique on the project builders in my software engineering laboratory class. Nothing in the past decade has so radically changed my own practice, or its effectiveness. The approach necessitates top-down design, for it is a top-down growing of the software. It allows easy backtracking. It lends itself to early prototypes. Each added function and new provision for more complex data or circumstances grows organically out of what is already there. The morale effects are startling. Enthusiasm jumps when there is a running system, even a simple one. Efforts redouble when the first picture from a new graphics software system appears on the screen, even if it is only a rectangle. One always has, at every stage in the process, a working system. I find that teams can grow much more complex entities in four months than they can build.

At the heart of the decoding problem is how to understand the vast information contained in neural signals, the challenge of what is being called "big data". For neuroscientists, big data is a means for exploring populations of neurons to discover the macroscopic signatures of dynamical systems, rather than attempting to make sense of the activity of individual neurons. Two surprising results from numerous experiments recording from neurons in different brain regions have revealed a wonderful secret of nature about the relation between the number of neurons recorded and and their dimensionality (the number of principal components required to explain a fixed percentage of variance). First, the dimensionality of the neural data is much smaller than the number of recorded neurons. Second, when dimensionality procedures are used to extract neuronal state dynamics, the resulting low-dimensional neural trajectories reveal portraits of the behavior of a dynamical system. This means that it may not be necessary to record from many more neurons within a brain region in order to accurately recover its internal state-space dynamics.

Alarmingly, our most fragile artifacts support either our most important revenue-generating systems or our most critical projects. In other words, the systems most prone to failure are also our most important and are at the epicenter of our most urgent changes. When these changes fail, they jeopardize our most important organizational promises, such as availability to customers, revenue goals, security of customer data, accurate financial reporting, and so forth.

At my “office” I wear an AR visor on my forehead. The visor is a curved band about hand width wide that is held a few inches away from my eyes for extra comfort during daylong use. The powerful visor throws up virtual screens all around me. I have about 12 virtual screens of all sizes and large data sets I can wrestle with my hands. The visor provides enough resolution and speed that most of my day I am communicating with virtual colleagues. But I see them in a real room, so I am fully present in reality as well. Their photorealistic 3-D avatar captures their life-size likeness accurately. My coworkers and I usually sit at a virtual table in a real room while we work independently, but we can walk around each other’s avatar. We converse and overhear each other just as if we are in the same room. It is so convenient to pop up an avatar that even if my real coworker is on the other side of the real room, we’ll just meet in the AR rather than walk across the gap.

The factors that usually decide presidential elections—the economy, likability of the candidates, and so on—added up to a wash, and the outcome came down to a few key swing states. Mitt Romney’s campaign followed a conventional polling approach, grouping voters into broad categories and targeting each one or not. Neil Newhouse, Romney’s pollster, said that “if we can win independents in Ohio, we can win this race.” Romney won them by 7 percent but still lost the state and the election. In contrast, President Obama hired Rayid Ghani, a machine-learning expert, as chief scientist of his campaign, and Ghani proceeded to put together the greatest analytics operation in the history of politics. They consolidated all voter information into a single database; combined it with what they could get from social networking, marketing, and other sources; and set about predicting four things for each individual voter: how likely he or she was to support Obama, show up at the polls, respond to the campaign’s reminders to do so, and change his or her mind about the election based on a conversation about a specific issue. Based on these voter models, every night the campaign ran 66,000 simulations of the election and used the results to direct its army of volunteers: whom to call, which doors to knock on, what to say. In politics, as in business and war, there is nothing worse than seeing your opponent make moves that you don’t understand and don’t know what to do about until it’s too late. That’s what happened to the Romney campaign. They could see the other side buying ads in particular cable stations in particular towns but couldn’t tell why; their crystal ball was too fuzzy. In the end, Obama won every battleground state save North Carolina and by larger margins than even the most accurate pollsters had predicted. The most accurate pollsters, in turn, were the ones (like Nate Silver) who used the most sophisticated prediction techniques; they were less accurate than the Obama campaign because they had fewer resources. But they were a lot more accurate than the traditional pundits, whose predictions were based on their expertise. You might think the 2012 election was a fluke: most elections are not close enough for machine learning to be the deciding factor. But machine learning will cause more elections to be close in the future. In politics, as in everything, learning is an arms race. In the days of Karl Rove, a former direct marketer and data miner, the Republicans were ahead. By 2012, they’d fallen behind, but now they’re catching up again.

Rather, you take the data you have and randomly divide it into a training set, which you give to the learner, and a test set, which you hide from it and use to verify its accuracy. Accuracy on held-out data is the gold standard in machine learning. You can write a paper about a great new learning algorithm you’ve invented, but if your algorithm is not significantly more accurate than previous ones on held-out data, the paper is not publishable. Accuracy on previously unseen data is a pretty stringent test; so much so, in fact, that a lot of science fails it. That does not make it useless, because science is not just about prediction; it’s also about explanation and understanding. But ultimately, if your models don’t make accurate predictions on new data, you can’t be sure you’ve truly understood or explained the underlying phenomena. And for machine learning, testing on unseen data is indispensable because it’s the only way to tell whether the learner has overfit or not.

We might be excused our ignorance in this case, because ocean-atmosphere systems are, after all, almost inconceivably complex. Less easy to excuse is our astounding lack of knowledge of much more visible features of our planet’s natural resources and ecology—features that have a direct impact on our well-being. For instance, we know surprisingly little about the state of the planet’s soils. While we have good information for some areas, like the Great Plains of the United States, soil data are sketchy for vast tracts of Africa, Asia, and Latin America, where billions of people depend directly on agriculture for survival. So we can’t accurately judge how badly we’ve degraded these soils through overuse and poor husbandry, though we do have patchy evidence that the damage is severe and getting worse in many places.18 Similarly, despite extensive satellite photography, our estimates of the rate and extent of tropical deforestation are rudimentary. We know even less about the natural ecology and species diversity inside these forests, where biologists presume most animal and plant species live. As a result, credible figures on the number of Earth’s species range from 5 to 30 million.19 And when it comes to broader questions—questions of how all these components of the planet’s ecology fit together; how they interact to produce Earth’s grand cycles of energy, carbon, oxygen, nitrogen, and sulfur; and how we’re perturbing these components and cycles—we find a deep and pervasive lack of knowledge, with unknown unknowns everywhere. Our ignorance, for all practical purposes, knows no bounds.

Descriptive statistics can be like online dating profiles: technically accurate and yet pretty darn misleading.

Recognizing that an AI agent cannot accurately infer your intentions means that it’s important to spell out each step of the process and be clear about what you want to accomplish.

What is a good correlation? How high should it be? These are commonly asked questions. I have seen several schemes that attempt to classify correlations as strong, medium, and weak. However, there is only one correct answer. The correlation coefficient should accurately reflect the strength of the relationship. Take a look at the correlation between the height and weight data, 0.705. It’s not a very strong relationship, but it accurately represents our data. An accurate representation is the best-case scenario for using a statistic to describe an entire dataset.

The machine takes all of these multitudinous pulls and forces which are fed in as data, and quickly computes the course of action which would be the most economical vector of need satisfaction in this existential situation. This is the behavior of our hypothetical person. The defects which in most of us make this process untrustworthy are the inclusion of information which does not belong to this present situation, or the exclusion of information which does. It is when memories and previous learnings are fed into the computations as if they were this reality, and not memories and learnings, that erroneous behavioral answers arise. Or when certain threatening experiences are inhibited from awareness, and hence are withheld from the computation or fed into it in distorted form, this too produces error. But our hypothetical person would find his organism thoroughly trustworthy, because all of the available data would be used, and it would be present in accurate rather than distorted form. Hence his behavior would come as close as possible to satisfying all his needs—for enhancement, for affiliation with others, and the like.

Scientific method is a set of data collection procedures that have been developed over a several hundred year period, procedures designed to collect data as cleanly and accurately as we possibly can.

You should see that a bigger sample makes for a shrinking standard error, which is how large national polls can end up with shockingly accurate results.

For example, a bank might want to make decisions about making financial loans in a more scientific way using data-based models. A decision-tree-based model could provide a consistently accurate loan decisions. Developing such decision tree models is one of the main applications of data mining techniques.

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Credit reporting agencies and other consumer data aggregators are extremely good at returning complete and accurate information even when identities are manipulated. It’s central to their business and there are extraordinary business and regulatory pressures on them to do this well. However, finding all of the relevant data sources, investigating documents provided in many different formats, putting it all together and offering a clear scoring decision is not something that can be easily accomplished.

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

Exploration is about formulating hypotheses or best guesses; confirmation is about rigorously testing preliminary conclusions. Confirmation turns best guesses into sure bets. As in scientific discovery, the less we know about a phenomenon, the more openended our questions. As relevant knowledge builds up, we become more precise about what we seek to learn, and we start to anticipate (more and more accurately) what we will find. Because hypothesis-testing experiments (for example, taking a new job on a provisional basis) are usually more costly than exploratory experiments (for example, working on a side project without leaving one’s job), we prefer to defer the former until we have solid data suggesting that we are going in the right direction. Variety for its own sake is not enough. In fact, a prolonged exploratory phase can be a defense mechanism against changing, and it can signal to others that we are not serious about making change. A true experimental method almost always leads to formulating new goals and new means to achieve them. As we learn from experience, we have to be willing to close avenues of exploration, to accept that what we thought we knew was wrong and that what we were hoping to find no longer suits

Research consistently shows that tougher individuals are able to perceive stressful situations as challenges instead of threats. A challenge is something that’s difficult, but manageable. On the other hand, a threat is something we’re just trying to survive, to get through. This difference in appraisals isn’t because of an unshakable confidence or because tougher individuals downplay the difficulty. Rather, those who can see situations as a challenge developed the ability to quickly and accurately assess the situation and their ability to cope with it. An honest appraisal is all about giving your mind better data to predict with.

Enhancing Diabetes Management: The Role of Blood Glucose Monitors from Med Supply US Introduction In the modern landscape of diabetes care, continuous monitoring of blood glucose levels has become an invaluable tool for individuals striving to manage their condition effectively. Among the pioneering names in this field is Med Supply US, a brand that has been making waves in New York, Miami, and Florida by offering state-of-the-art Continuous Glucose Monitoring (CGM) services. Let's delve into how these Blood Glucose Monitors are revolutionizing diabetes management. Continuous Glucose Monitoring: A Game-Changer Gone are the days of frequent finger pricks and sporadic glucose checks. Med Supply US is at the forefront of empowering individuals with diabetes to monitor their blood glucose levels seamlessly and gain insights in real-time. CGMs have ushered in a new era of convenience, accuracy, and comprehensive data analysis, allowing for a more proactive approach to diabetes care. Benefits of CGMs by Med Supply US Med Supply US has carved a niche for itself in the diabetes management landscape, offering a range of benefits that set it apart: Accurate Monitoring: CGMs provide continuous readings throughout the day and night, eliminating the guesswork associated with traditional glucose monitoring methods. This accuracy is pivotal for making informed decisions about diet, medication, and activity levels. Real-time Data: With CGMs, individuals receive real-time data on their glucose levels. This not only keeps them informed but also enables prompt action in response to fluctuations, reducing the risk of extreme highs and lows. Trend Analysis: Med Supply US's CGMs offer comprehensive data analysis, highlighting trends and patterns in glucose levels over time. This aids in identifying factors that impact blood sugar, thus facilitating better management strategies. Alerts and Notifications: CGMs from Med Supply US come equipped with customizable alerts and notifications. This feature helps users stay vigilant about their glucose levels, especially during critical moments.

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It also got a boost from a new online “Fit Finder” quiz, which replaced the smartphone sizing app in 2016. Because the app was tricky to use and was available only for iPhone owners, lead designer Ra’el Cohen worked with ThirdLove’s data team to develop a detailed questionnaire that was as accurate as the app in determining a customer’s size. It walked website visitors through a series of questions about their current bra—the maker, the size, the fit of the cup (cups gape a little … cups overflow a lot), band, and straps. And it asked them to select, from a series of drawings of different-shaped breasts, which pair most resembled theirs. Among the nine options: Asymmetric (one breast is larger than the other), Bell (slimmer at the top, fuller at the bottom), East West (nipples point outward, in opposite directions). By 2018, eleven million women had taken the Fit Finder quiz,

Queer data is more than using data to tell stories about the lives and experiences of LGBTQ individuals: the presentation of the data is also an opportunity for LGBTQ people to see themselves reflected, although this mirror image is never a truly accurate representation.

Considering the Scottish census through these theoretical lenses, where the census is not a neutral representation of a reality but a tool to construct a governable population, raises questions as to whether the census is an exercise in knowledge construction or a tool to bolster the state’s capacity to manage its population. These two objectives are not exclusive: improved knowledge likely facilitates the design of more efficient ways to coerce, control and discipline people who live within a state's jurisdiction. However, if the construction of knowledge is no longer the primary purpose of a census, this throws into doubt then need for a census to collect accurate information that authentically represents the lives and experiences of the people about whom the data relates.

The health benefits from regular activity are widely acknowledged and can be achieved by any adult willing to complete the weekly target of just 150 minutes of moderate intensity physical activity. This is the equivalent of just under 22 minutes per day so we would hardly be surprised if most able-bodied adults achieved these targets. Yet, survey data in the United States suggests that only 49 per cent of adults achieve these minimum recommendations, although some states fare better. For example, 60 per cent of Alaskans meet the minimum recommendations compared to only 39 per cent of Louisianans. Adults in the United Kingdom appear to struggle even more, with only 35 per cent of men and women achieving the same 150 minute weekly target. To make matters worse, these percentages are all based on official government statistics which were obtained by asking random samples of people to estimate how much activity they usually do. Using these types of self-report questionnaires introduces considerable bias, especially when the respondents are aware that they don’t do as much exercise as they believe they should. A better way to check how much exercise adults really do is to use electronic sensors worn on the body to record the number of minutes spent performing physical activity of moderate intensity or above. Using this more accurate measurement technique, only 6 per cent of men and 4 per cent of women in the United Kingdom actually achieved the minimum weekly amounts of recommended physical activity. Similar results have been revealed in other Western countries, including the United States. If most adults believe that regular exercise is important, then the low participation statistics suggest that it must be difficult to achieve in practice.

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.

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

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Whatever the reason, the existence of some persistent investment factors is today accepted by almost every (if not all) financial economist and investor. In an ingenious bit of marketing, factors are often called “smart beta.” Sharpe himself grew to hate the term, as it implies that all other forms of beta are dumb.10 Most financial academics prefer the term “risk premia,” to more accurately reflect the fact that they think these factors primarily yield an investment premium from taking some kind of risk—even if they cannot always agree what the precise risk is. An important milestone was when Fama and his frequent collaborator Ken French—another Chicago finance professor who would later also join DFA—in 1992 published a paper with the oblique title “The Cross-Section of Expected Stock Returns.”11 It was a bombshell. In what would become known as the three-factor model, Fama and French used data on companies listed on the NYSE, the American Stock Exchange, and the Nasdaq from 1963 to 1990 and showed that both value (the tendency of cheap stocks to outperform expensive ones) and size (the tendency of smaller stocks to outperform bigger ones) were distinct factors from the broader market factor—the beta. Although Fama and French’s paper termed these factors as rewards for taking extra risks, coming from the father of the efficient-markets hypothesis, it was a signal event in the history of financial economics.12 Since then academics have identified a panoply of factors, with varying degrees of durability, strength, and acceptance. Of course, factors do not always work. They can go through long fallow stretches where they underperform the market. Value stocks, for example, suffered a miserable bout of performance in the dotcom bubble, when investors wanted to buy only trendy technology stocks. And to DFA’s chagrin, after small caps enjoyed a robust year in DFA’s first year of existence, they would then undergo a long, painful seven-year period of trailing dramatically behind the S&P 500.13 DFA managed to keep growing, losing very few clients, partly because it had always stressed to them that stretches like this could happen. But it was an uncomfortable period that led to many awkward conversations with clients.

Perfection—complete accuracy—is the goal of every map, but it’s not actually . . . it’s not actually possible, William. Even for us. Even if we could manage to make the Haberson Map completely accurate for every single data point for one instant, the world is always changing. Something will shift, and we’ll be right back to square one again.

The success of product development and launch strategies for businesses in all industries is greatly influenced by best market research companies in Myanmar. These businesses enable businesses to make informed decisions that drive innovation and market penetration by providing valuable insights into consumer behaviors, market trends, and competitive landscapes. In today's dynamic business environment, it is essential for organizations looking to create competitive products and launch them successfully to comprehend the significance of market research. This article looks at how market research companies play a variety of roles in product development and launch strategies. It emphasizes the importance of working together, making decisions based on data, and taking advantage of market research trends for long-term growth and market leadership. 1. Companies' Introduction to Market Research An Overview of Business Market Research Market research is essential for helping businesses comprehend their market, competition, and customers. To make decisions based on accurate information, it involves gathering and evaluating data. Contribution of best market research companies in Myanmar to Product Development Market research firms are specialized in collecting insights that can be used to inform product development strategies for businesses. They assist in determining the demand for new products, assessing consumer behavior, and identifying market opportunities. 2. Understanding Consumer Needs and Preferences is Critical to Product Development Market research provides businesses with in-depth insights into consumer preferences, needs, and actions. In order to create products that resonate with the intended audience, this understanding is essential. Research can be used to assess market demand and potential, allowing businesses to make decisions about product development based on data. New products' chances of success are boosted while risks are reduced. 3. Utilizing Market Research for Launch Strategies Market Segmentation and Targeting Market research enables businesses to divide their target market into subsets based on behavior, psychographics, and demographics. For a more successful product launch, this segmentation helps tailor marketing strategies to specific consumer groups. Pricing and Positioning Strategies Businesses can figure out the best pricing strategy for their products based on consumer perceptions, competitor pricing, and market trends by conducting market research. Additionally, research aids in effectively positioning products in the market to set them apart from rivals. 4. Collaborating with Market Research Firms Choosing the Right Market Research Partner It is essential to select the right market research partner in order to obtain insights that are both accurate and applicable. Expertise, industry experience, and the capacity to provide insights that are in line with the company's goals should all be taken into account. Creating Effective Research Briefs Businesses should provide their market research partner with concise and in-depth research briefs to maximize the value of the research. A clearly defined brief assists in ensuring that the research is focused, pertinent, and in line with the company's objectives. 5. Studies of Cases: Product Launches That Work: The Importance of Market Research Market research is essential to the success of product launches. We are able to see the direct impact of market research on product development strategies by analyzing real-world examples. Market research companies offer valuable insights that can make or break a product launch, from comprehending consumer preferences to determining market gaps. ### Impact of Market Research on Product Launches Market research has a significant impact on product launches because it ensures that businesses know what their target audience wants and needs. Companies are able to effectively f

market research consultant in india: AMT Market Research Having accurate and insightful market research is essential for making informed decisions in today's dynamic business environment. AMT Market Research, a prominent Indian market research consultant, specializes in providing custom solutions to assist businesses in navigating the Indian market's complexities. AMT Market Research aids businesses in a variety of industries in locating growth opportunities, mitigating risks, and remaining competitive by having a thorough comprehension of local consumer behavior, economic trends, and industry shifts. Services and Expertise AMT Market Research offers a wide range of services tailored to each client's specific requirements. These are some: Market Analysis By conducting a thorough market analysis, AMT assists businesses in comprehending market share, size, and trends. AMT ensures that businesses have the data they need to make strategic decisions by evaluating key industry drivers, competitive landscapes, and potential growth areas. Customer Insights Any business that wants to succeed in India's vast and varied market must have a solid understanding of consumer behavior. Businesses can use AMT's consumer insights services to create targeted products and marketing strategies by delving deeply into buying patterns, preferences, and motivations. By analyzing competitors' strategies, strengths, weaknesses, and market positioning, competitor analysis from AMT aids businesses in benchmarking. By taking advantage of their distinct value propositions and comprehending the dynamics of the competition, this service enables businesses to maintain their lead. AMT's feasibility studies provide a comprehensive analysis of potential outcomes prior to launching a new product, entering a new market, or expanding operations, assisting clients in assessing risks and profitability. Data Collection and Analysis AMT uses surveys, interviews, and focus groups to collect both qualitative and quantitative data. Advanced analytics are used by the company to transform unstructured data into useful insights, giving businesses a clear path forward. What Attracts You to AMT Market Research? AMT Market Research stands out because it is able to provide individualized solutions that address the particular difficulties that the Indian market faces. AMT provides insights that are accurate, timely, and applicable thanks to a team of seasoned professionals. Clients will be able to anticipate and prepare for changes thanks to their data-driven approach. AMT is a dependable partner for businesses looking to expand in India or strengthen their market position because of its extensive network across various industries and unparalleled access to market information. market research consultant in india can help you stay ahead of the competition, whether you're a local business or a multinational corporation. In conclusion, businesses aiming for success in India need AMT Market Research as a crucial partner. AMT helps its customers make well-informed decisions that drive growth and profitability by providing individualized research solutions, consumer insights, and strategic analysis. AMT Market Research is the preferred consulting firm for businesses attempting to navigate the Indian market's complexities.

best market research companies in Myanmar: With AMT Market Research, you can learn more about Myanmar, a new market with a lot of potential. It is becoming a popular destination for businesses looking to expand in Southeast Asia. However, a thorough comprehension of the local consumer behavior, trends, and regulatory frameworks is necessary for successfully navigating this dynamic and rapidly changing landscape. AMT Market Research, one of the best market research companies in Myanmar, steps in to help businesses thrive by providing actionable insights and data-driven strategies. What Attracts You to AMT Market Research? AMT Market Research is well-known for providing customized, dependable, and comprehensive market research services. With a solid presence in Myanmar, AMT has been at the bleeding edge of assisting both neighborhood and worldwide organizations with figuring out the complexities of this one of a kind market. AMT stands out as one of the best market research companies in Myanmar for the following reasons: Local Knowledge: Myanmar is a nation with distinctive social, cultural, and ec onomic characteristics. AMT Market Research employs seasoned professionals who are well-versed in the dynamics of the local market. They provide in-depth knowledge of consumer behavior, upcoming trends, and potential obstacles unique to Myanmar's market. A Variety of Services: AMT Market Research offers a wide range of services, such as consumer research, competitor analysis, brand positioning, and product testing. Each client receives a service that is tailored to meet their specific requirements, ensuring that insights are accurate and actionable. Insights Driven by Data: To collect data, AMT makes use of cutting-edge research methods like qualitative and quantitative methods. AMT makes sure that the data it collects—from focus groups and surveys to in-depth interviews and field studies—is relevant and aids businesses in making informed decisions. Research on a Specific Sector: AMT Market Research provides industry-specific studies for businesses in the retail, telecom, healthcare, FMCG, and financial sectors. Businesses can more effectively target their audience and optimize their strategies using precise data thanks to this sector-specific approach. Strategic Entry into a Market: AMT provides strategic insights that can assist businesses attempting to navigate the complexities of market entry for the Myanmar market. AMT assists businesses in avoiding costly errors and accelerating growth by comprehending regulatory frameworks and determining the appropriate distribution channels. AMT Market Research's Advantages Accurate Data Collection: Get a clear picture of the market by having access to accurate, real-time data. Recommendations for Taking Action: AMT provides recommendations that assist businesses in taking immediate action in addition to providing data. Cost-effective Options: AMT Market Research is a cost-effective option for businesses of all sizes because they offer competitive pricing for their services. Conclusion: AMT Market Research is your go-to partner if you want your business in Myanmar to succeed long-term and with knowledge. AMT is one of the best market research companies in Myanmar thanks to their data-driven approach, extensive expertise, and wide range of services. Partner with AMT Market Research right away to empower your business with important insights!

market research survey in Myanmar– AMT Market Research Myanmar, a nation in Southeast Asia that is rapidly developing, presents numerous business opportunities for both domestic and foreign businesses. However, it is essential to gain a comprehensive understanding of the environment before making strategic business decisions due to the unique socio-economic landscape, consumer behavior, and market conditions. AMT Market Research serves as a reliable partner in this regard, providing Myanmar market research surveys that are comprehensive and insightful. Why market research survey in Myanmar Is Important Myanmar's economic structure is undergoing significant change due to increased foreign investment, a growing middle class, and rapid urbanization. However, there are difficulties associated with this expansion. Businesses need to know a lot about the local market because of the country's diverse population, changing regulatory landscape, and changing consumer preferences. In Myanmar, crucial insights into customer requirements, preferences, and purchasing patterns can be gleaned from a well-conducted market research survey. It helps businesses navigate challenges unique to this region, comprehend market trends, and identify potential growth opportunities. When it comes to conducting surveys for market research survey in Myanmar, AMT Market Research stands out as a leading name. AMT is the ideal partner for businesses seeking actionable insights because it has a team of highly skilled professionals and years of experience and is well-versed in the complexities of the Myanmar market. Services Provided by AMT Market Research Consumer Behavior and Insights: AMT focuses on gaining an understanding of consumer behavior by collecting information about preferences, purchasing patterns, and the factors that influence decision-making processes. Companies that want to tailor their products or services to local demand need to know this. Methods for Entering the Market: AMT provides invaluable information regarding competitors, market size, and potential obstacles for businesses wishing to enter the Myanmar market. You can come up with a solid plan for entering and thriving in the local market thanks to their research. Specific Industry Research: AMT conducts industry-specific market research surveys in Myanmar for businesses in the manufacturing, healthcare, telecom, and retail sectors, among other industries. This aids businesses in comprehending the industry-specific opportunities and threats as well as the competitive landscape. Positioning and Perception of the Brand: It's important to know how your brand is seen in Myanmar. Businesses can use the insights gained from AMT surveys to improve their market positioning by increasing brand awareness, customer loyalty, and satisfaction. Solutions for Personalized Research: AMT provides individualized research solutions based on your particular requirements. AMT tailors its research methods to provide the most pertinent and actionable data, regardless of whether you're looking for qualitative insights, quantitative data, or a combination of the two. What Attracts You to AMT Market Research? Local Knowledge: AMT Market Research is well-equipped to provide insights that really matter because they have a deep understanding of Myanmar's particular market dynamics. Complete Information: Because their surveys aim to cover every facet of the market, you'll get a comprehensive picture of the opportunities and challenges. Relevant Insights: AMT's data is more than just numbers and figures; it also contains meaningful insights that can guide business strategies and decisions. Timely and dependable reports: AMT's reputation for timely, accurate, and comprehensive reports will keep you ahead of the competition in the Myanmar market. Businesses looking to establish or expand their presence in Myanmar's emerging market must conduct a market research survey. Y

1. Understanding Myanmar's Market Research: The Function of AMT Market Research In the rapidly changing economic landscape of Myanmar, businesses are increasingly recognizing the significance of making well-informed decisions based on complete market insights. One of the central members driving this development is AMT Statistical surveying, a main market research survey in Myanmar which has laid out its presence in Myanmar. With a populace of more than 54 million, Myanmar is a country wealthy in assets and potential. Be that as it may, its market is perplexing, impacted by a heap of elements like social variety, monetary vacillations, and administrative changes. Organizations need accurate data and insights to effectively navigate this complexity, and AMT Market Research meets this need. AMT Market Research has established itself as one of the best market research firms by employing cutting-edge techniques tailored to Myanmar's particular landscape. They use a combination of qualitative and quantitative research methods to get a complete picture of the market. From buyer conduct investigation to cutthroat scene appraisals, AMT gives priceless bits of knowledge that assist organizations with pursuing informed vital choices. market research survey in Myanmar is one of AMT's most distinctive methods. AMT enables businesses to comprehend preferences, purchasing habits, and emerging trends by directly engaging with customers and gathering firsthand feedback. Businesses can strategically tailor their offerings thanks to this grassroots approach, which not only reveals what consumers want but also identifies market gaps. AMT' market research survey in Myanmar, on top of that, are designed to be comprehensive yet effective. They use a combination of online surveys, focus groups, and in-person interviews to get responses from a wide range of people from different demographic groups. By collecting data in a variety of ways, businesses can reach a wider audience while also focusing on specific markets. It is essential to have an understanding of socioeconomic factors in a market that is still in its infancy. In their surveys, AMT Market Research emphasizes the significance of demographic insights. They assist businesses in developing targeted marketing strategies that resonate with their intended audience by taking into account variables such as education levels, income levels, and regional differences. This scientific thoroughness guarantees that suggestions are information driven as well as mirror the social and monetary real factors of the customers. Another thing that sets it apart is the company's dedication to conducting research in an ethical manner. AMT Market Research's core values of honesty, integrity, and dependability help to build trust with clients and respondents alike. Organizations can feel sure that the bits of knowledge gave are precise as well as gathered with deference for members' privileges and information security. The demand for high-quality market research will only grow as the economy of Myanmar continues to mature and the market attracts more attention from around the world. AMT Market Research positions itself as a crucial partner for businesses looking to enter or expand into the Myanmar market and is prepared to meet this demand. They are at the forefront of this ever-evolving sector because of their expertise and local knowledge. In conclusion, AMT Market Research provides essential tools and insights that can aid in strategic planning and execution for businesses trying to navigate the complexities of Myanmar's market. They play a crucial role in shaping the future of businesses in Myanmar through their commitment to ethical practices and comprehensive market research surveys. Associations looking for development ought to think about utilizing AMT's ability to open the potential inside this promising business sector.

The art of accounting and finance is the art of using limited data to come as close as possible to an accurate description of how well a company is performing.

A perfect pattern or model is one that (a) accurately describes a situation, (b) is broadly applicable, and (c) can be described in a simple manner.

There are no opinions in life if accurate data is available.

The Pseudocode Programming Process Have you checked that the prerequisites have been satisfied? Have you defined the problem that the class will solve? Is the high-level design clear enough to give the class and each of its routines a good name? Have you thought about how to test the class and each of its routines? Have you thought about efficiency mainly in terms of stable interfaces and readable implementations or mainly in terms of meeting resource and speed budgets? Have you checked the standard libraries and other code libraries for applicable routines or components? Have you checked reference books for helpful algorithms? Have you designed each routine by using detailed pseudocode? Have you mentally checked the pseudocode? Is it easy to understand? Have you paid attention to warnings that would send you back to design (use of global data, operations that seem better suited to another class or another routine, and so on)? Did you translate the pseudocode to code accurately? Did you apply the PPP recursively, breaking routines into smaller routines when needed? Did you document assumptions as you made them? Did you remove comments that turned out to be redundant? Have you chosen the best of several iterations, rather than merely stopping after your first iteration? Do you thoroughly understand your code? Is it easy to understand?

Confidentiality - seeks to prevent the unauthorized disclosure of information: it keeps data secret • Integrity - seeks to prevent unauthorized modification of information. In other words, integrity seeks to prevent unauthorized write access to data. Integrity also seeks to ensure data that is written in an authorized manner is complete and accurate. • Availability - ensures that information is available when needed • Subject - An active entity on an information system • Object - A passive data file • Annualized Loss Expectancy—the cost of loss due to a risk over a year • Threat—a potentially negative occurrence • Vulnerability—a weakness in a system • Risk—a matched threat and vulnerability • Safeguard—a measure taken to reduce risk • Total Cost of Ownership—the cost of a safeguard • Return on Investment—money saved by deploying a safeguard

Using an Internet search engine to learn more about these sayings can be both a revelatory and exasperating experience. Search engines contain link after link to websites with faulty information, repetitive text, and incomplete data. Moving beyond this mélange of misinformation is nearly impossible for the average web user. It’s no wonder, then, that such mistakes are perpetuated and duplicated to the extreme. Many truth-seekers have struggled with the cacophony of conflicting information online; too often, accurate data is overwhelmed by inaccurate 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.

However, in other circumstances, such as with PSR 1913 + 16, the situation is very different, and gravitational radiation from the system indeed has a significant role to play. Here, Einstein's theory provides a firm prediction of the detailed nature of the gravitational radiation that the system ought to be emitting, and of the energy that should be carried away. This loss of energy should result in a slow spiralling inwards of the two neutron stars, and a corresponding speeding up of their orbital rotation period. Joseph Taylor and Russell Hulse first observed this binary pulsar at the enormous Aricebo radio telescope in Puerto Rico in 1974. Since that time, the rotation period has been closely monitored by Taylor and his colleagues, and the speed-up is in precise agreement with the expectations of general relativity (cf. Fig. 4.11). For this work, Hulse and Taylor were awarded the 1993 Nobel Prize for Physics. In fact, as the years have rolled by, the accumulation of data from this system has provided a stronger and stronger confirmation of Einstein's theory. Indeed, if we now take the system as a whole and compare it with the behaviour that is computed from Einstein's theory as a whole-from the Newtonian aspects of the orbits, through the corrections to these orbits from standard general relativity effects, right up to the effects on the orbits due to loss of energy in gravitational radiation-we find that the theory is confirmed overall to an error of no more than about 10^-14. This makes Einstein's general relativity, in this particular sense, the most accurately tested theory known to science!

Texas sharpshooter fallacy: Imagine that you are driving down a country road in Texas. You see a barn that has six targets painted on it, and a bullet hole at the very center of each target. “Yes sir,” says the owner of the barn, “I never miss.” “That’s right,” says his spouse, “there ain’t a man in the state of Texas who’s more accurate with a paint brush.” Got it? He fired the six shots, and then painted the targets around them.

foundational attitude for which we pray is that we might desire what gives God glory more deeply than we desire any other created reality. This spiritual freedom is so radical and so beyond our power to create in ourselves that it brings us face to face with our own poverty and our need for continual prayer. Sometimes all we can muster is the desire to desire what God desires! But any degree of hunger, any desire for God, any seeking of God’s call already happens through God’s Spirit, and God accepts it as enough. Over time, through repeated discernments and through daily living of one’s Christian life, this desire can become an increasingly natural and habitual orientation. As that transformation occurs, we experience deeper and deeper spiritual freedom. 2. Discover and name the issue or choice you face. What is really at stake is not always self-evident. An ambiguous or sprawling issue can obscure or even prevent subsequent discernment. Carefully framing the issue not only helps to clarify the matter for discernment, but it also actually begins the process of sifting and discriminating that is at the heart of discernment. 3. Gather and evaluate appropriate data about the issue. Discernment is not magic. We have to do our homework. The efficacy of the subsequent decision can rise or fall on obtaining accurate and relevant information about various options and their implications. However, since decision making is not identical to discernment, it is possible to botch a decision while still advancing in discernment. Fortunately, through grace, it is quite possible to grow in discipleship, manifest greater spiritual freedom, and hunger more strongly for what God desires in the midst of a failed decision. But prudence demands that we do the homework necessary. 4. Reflect and pray. Actually we have been praying from the outset. We pray for spiritual freedom. We select and frame the issue for discernment in prayer. We prayerfully select and consider the relevant data. But as we begin the process of discrimination in a more focused way, it is important to renew our attention to prayer. 5. Formulate a tentative decision. Many different methods can help us come to a decision, and therefore aid our discernment. We will explore seven methods in the entry points in this book, but many options exist in the tradition. Discerned decision making can employ any decision-making process, whether traditional or newly created, that fits the material being

Despite the messiness of the input, Google’s service works the best. Its translations are more accurate than those of other systems (though still highly imperfect).

market. Instead, we need to keep in mind that the representation is just a shortcut for presenting a lot of information, or even worse, a distortion of that information. Reliability bias People assume that something is accurate when it may not be. For example, market data that you use in your historical testing or that come to you live are often filled with errors. Unless you assume that errors can and do exist, you may make lots of mistakes in your trading and investing decisions. Lotto bias People want to control the market, and so they tend to focus on entry, where they can “force” the market to do a lot of things before they enter. Unfortunately, once they enter, the market is going to do what the market is going to do. And

Today we aren’t quite to the place that H. G. Wells predicted years ago, but society is getting closer out of necessity. Global businesses and organizations are being forced to use statistical analysis and data mining applications in a format that combines art and science–intuition and expertise in collecting and understanding data in order to make accurate models that realistically predict the future that lead to informed strategic decisions thus allowing correct actions ensuring success, before it is too late . . . today, numeracy is as essential as literacy. As John Elder likes to say: ‘Go data mining!’ It really does save enormous time and money. For those

Peter Steiner’s 1993 cartoon in the New Yorker captured the promise of freedom and anonymity that the Internet once offered: “On the Internet, nobody knows you’re a dog.” But our era of big data—especially as more data are captured and at finer-grain resolutions—flips this premise around. Our behaviors online, in virtual worlds, and when using smart mobile devices allow others to make accurate inferences about who we are and what we like. On the Internet, everybody knows you’re a dog. And as marketers and advertisers compete over this growing flood of data, the facts they learn about each of us are likely to become more and more unsettling.

Location is also predictive. Researchers at Microsoft found that location data can be used to predict fairly accurately where people will be located in the future.

Do not assume that a source agrees with a writer when the source summarizes that writer’s line of reasoning. Quote only what a source believes, not its account of someone else’s beliefs, unless that account is relevant. 2.  Record why sources agree, because why they agree can be as important as why they don’t. Two psychologists might agree that teenage drinking is caused by social influences, but one might cite family background, the other peer pressure. 3.  Record the context of a quotation. When you note an important conclusion, record the author’s line of reasoning: Not Bartolli (p. 123): The war was caused … by Z. But    Bartolli: The war was caused by Y and Z (p. 123), but the most important was Z (p. 123), for two reasons: First,… (pp. 124–26); Second,… (p. 126) Even if you care only about a conclusion, you’ll use it more accurately if you record how a writer reached it. 4.  Record the scope and confidence of each statement. Do not make a source seem more certain or expansive than it is. The second sentence below doesn’t report the first fairly or accurately. One study on the perception of risk (Wilson 1988) suggests a correlation between high-stakes gambling and single-parent families. Wilson (1988) says single-parent families cause high-stakes gambling. 5.  Record how a source uses a statement. Note whether it’s an important claim, a minor point, a qualification or concession, and so on. Such distinctions help you avoid mistakes like this: Original by Jones: We cannot conclude that one event causes another because the second follows the first. Nor can statistical correlation prove causation. But no one who has studied the data doubts that smoking is a causal factor in lung cancer. Misleading report: Jones claims “we cannot conclude that one event causes another because the second follows the first. Nor can statistical correlation prove causation.” Therefore, statistical evidence is not a reliable indicator that smoking causes lung cancer.

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.

[...] when you give children a pattern of data that supports an unlikely hypothesis, the children are actually better than the adults at figuring out that unlikely hypothesis. The children are actually more likely to update their belief when they get new data. Adults are more likely to just rely on the things that they already heard, or the things that people have told them. It’s stupid in some ways, but, of course, in other ways it’s very practical, because if every time you have to make a decision you had to go back from scratch and think, “Do I believe it’s really true? Is it really accurate?” it would be hard to act.

The Importance of Bookkeeping for Business Success Bookkeeping is a vital component of any business, regardless of its size or industry. It involves the accurate recording of financial transactions, which provides essential data for financial reporting, decision-making, and tax compliance. By maintaining well-organized and detailed financial records, businesses can ensure long-term stability and growth. What is Bookkeeping? Bookkeeping refers to the process of systematically recording a company’s financial transactions, including income, expenses, payroll, and other financial activities. It provides the foundation for creating financial statements such as balance sheets and income statements. Without proper bookkeeping, businesses would struggle to maintain accurate records, which could lead to financial mismanagement and compliance issues. Benefits of Professional Bookkeeping One of the most important benefits of bookkeeping is improved financial management. Professional bookkeepers help businesses maintain accurate and up-to-date records, ensuring that every transaction is tracked and categorized correctly. This level of organization allows business owners to monitor their cash flow, identify areas where they can cut costs, and make informed decisions. Additionally, by outsourcing bookkeeping, businesses can focus on their core operations without worrying about financial details. Tax Compliance and Preparation Tax season can be stressful for businesses, but proper bookkeeping makes it much easier. When financial records are well-maintained throughout the year, tax preparation becomes a seamless process. Bookkeepers ensure that all income and expenses are accurately recorded, allowing businesses to file their taxes without errors. Moreover, organized records help businesses take advantage of tax deductions and avoid penalties for late or inaccurate filings. Financial Reporting and Growth Accurate bookkeeping also plays a key role in generating financial reports. These reports provide insights into a business’s profitability, cash flow, and overall financial health. With this information, business owners can plan for future growth, make strategic investments, and secure loans if needed. Without reliable financial data, making informed decisions becomes much more difficult. In conclusion, bookkeeping is an essential practice for any business. By ensuring accurate financial records, tax compliance, and detailed reporting, businesses can achieve greater financial stability and growth opportunities.