Data Analysis Quotes

If you torture the data long enough, it will confess.

I would defer to your expertise in shooting and killing things. You should defer to mine in data analysis.

When it comes to riding a trend for business growth, there are three important steps that we should always remember: data analysis, trend identification, and fast and effective decision making.

We ought to regard the present state of the universe as the effect of its antecedent state and as the cause of the state that is to follow. An intelligence knowing all the forces acting in nature at a given instant, as well as the momentary positions of all things in the universe, would be able to comprehend in one single formula the motions of the largest bodies as well as the lightest atoms in the world, provided that its intellect were sufficiently powerful to subject all data to analysis; to it nothing would be uncertain, the future as well as the past would be present to its eyes. The perfection that the human mind has been able to give to astronomy affords but a feeble outline of such an intelligence.

Once you have analyzed data, you have to mine that data to find insights from it. At this point, you can involve your marketing or product team to work with the data analysis team.

In current times, we have access to so much data. Having said that, data analysis can uncover so many hidden patterns about customer behavior and how they interact with various products.

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

To find signals in data, we must learn to reduce the noise - not just the noise that resides in the data, but also the noise that resides in us. It is nearly impossible for noisy minds to perceive anything but noise in data.

An analysis of 248 performance reviews collected from a variety of US-based tech companies found that women receive negative personality criticism that men simply don’t.7 Women are told to watch their tone, to step back. They are called bossy, abrasive, strident, aggressive, emotional and irrational. Out of all these words, only aggressive appeared in men’s reviews at all – ‘twice with an exhortation to be more of it’.

You must realize from your studies, Miss Feng, with the complexity of our MEG society, algorithms have become indispensable for analysis and decision making in our data-saturated environment. Digitization creates information beyond the processing capacity of Human intelligence, yet provides a stable mental environment powered by a set of logical rules. That is how we keep order in Toronto MEG.” “Excuse me, Mr. Zhang,” Ke Hui said, somewhat uncomfortably, “but the invisibility of algorithmic systems and the obscurity of their operations hint at a society where algorithms do not reflect the public interest. Issues involving ethics and values I mean, from my reading of MEG history, challenge the assumptions of the neutrality of algorithmic systems. Would this not undermine democratic governance through reliance on technocratic resolutions?

Data is a form of capital. And as is the case with all capital - it has to be efficient utilized.

In this era of fake news and paid news artificial intelligence is more and more used as a political tool to manipulate and dictate common people, through big data, biometric data, and AI analysis of online profiles and behaviors in social media and smart phones. But the days are not far when AI will also control the politicians and the media too.

Computers bootstrap their own offspring, grow so wise and incomprehensible that their communiqués assume the hallmarks of dementia: unfocused and irrelevant to the barely-intelligent creatures left behind. And when your surpassing creations find the answers you asked for, you can't understand their analysis and you can't verify their answers. You have to take their word on faith.

That’s the chain of thinking: D-A-D-A. Getting data leads to analysis. Analysis leads to a decision. A decision leads to an action. Simple. That’s how thinking works.

In the business people with expertise, experience and evidence will make more profitable decisions than people with instinct, intuition and imagination.

The truth is that every business answer that you’re looking for is out there—covered and surrounded by many layers of unnecessary data.

There is no better tool to bring you closer to your competitors than market research. So, keep your friends close and your competitors even closer with the help of market research.

Market research gives you enough time to learn from your competitors’ mistakes, take inspiration from their strengths, and exploit their weaknesses.

Good market research would have made you aware of the real picture at the right time, and maybe you would have been able to write a different future because of that.

Let market research be a permanent, ongoing part of your business strategy.

You don’t have to wait until you have a heart attack to understand the importance of a healthy lifestyle.

A market research project starts when you have the answers to the following questions: 1. Why are you researching? 2. What are you going to do with the results?

Having an objective for any project is highly important as we are living in a world full of data—some useful but mostly useless.

For identifying the objective of your market research project, it is highly advisable that you should zero in on the exact information you want to collect and from who.

If you are going to use the results of market research to make a big business decision, then it’s a good idea to do quantitative research rather than qualitative.

The more numbers you know through market research, the more you will be able to cut down your business risk.

The biggest advice I can give for setting up your market research objectives is to be very clear and concise.

Two things happen when your objectives are too broad—you don’t achieve the right results and you lose a lot of your resources. You want to avoid both of those.

If your objectives are too broad, they can dilute your project.

One market research project should have only one objective. More than one objective can affect the effectiveness of your research.

Never guess anything. You will make bad business decisions if you do that. If you don’t have data on something, start a research project on that topic.

Discovering a cure for a disease is one thing, but making that cure available to everyone so it can actually be used to eradicate the problem is another thing.

The best way to improve your decision-making and decrease your business risk is to use data to guide your decisions.

Interviews are a qualitative form of collecting data. The reason it generates good responses is because it’s way more personal than other forms of data gathering techniques.

Your market research project is of no use if you are not going to use the results of it in your decision-making process.

By the time your perfect information has been gathered, the world has moved on.

If you can't understand a study, the problem is with the study, not with you.

Your market research objectives need to fit into your marketing strategy. If your objectives are not supporting your marketing strategy, then it’s going to be a waste of your resources.

All the data you have collected is of no use if you don’t know how to gain insights from it, how to make profitable decisions with the help of this, and how to put your data into action.

Refining a data set implies getting rid of unnecessary, duplicate, or misleading data. If you don't do that, you can see results that are not actually true, thus hurting your decision-making.

The male-unless-otherwise-indicated approach to research seems to have infected all sorts of ethnographic fields. Cave paintings, for example, are often of game animals and so researchers have assumed they were done by men - the hunters. But new analysis of handprints that appear alongside such paintings in cave sites in France and Spain has suggested that the majority were actually done by women.

To save ourselves from getting lost in this sea of data and ending up directionless, it becomes vital for every business owner to not just set up their market research objectives but also to stick to those.

Data is one of the most valuable resources of a bank.

We've passed through the era of data accumulation. We're entering now into the era of data amalgamation - data combined and directed in service of a greater purpose.

The real point of the matter is that what we call a 'wrong datum' is one which is inconsistent with all other known data. It is our only criterion for right and wrong.

If the research is done for brand awareness and the results don't show any rosy picture, then maybe you want to share those results with your marketing head who can include more brand awareness strategies in her marketing plan.

Break down your problem as much as you can, but don’t do it on the basis of your guesses. If you don’t know exactly what is wrong, use market research to break down your problem further and further until you reach the very point of your trouble.

Without solid insights gained through market research, any kind of marketing you do is like throwing your pamphlets at Times Square and hoping somebody will pick them up, read them, become interested, and get the product. That happens… just not so often.

The latest technologies are often sexy, but beware of solutions that vendors dress up like trollops, unless you're looking for a one-night stand.

What I need I carry in my head. Everything in that machine came from me. My fat burned into knowledge. My calories pedaled into data analysis" -- The Calorie Man

To her data analysis was the ugly love child of science and Kafka,

In this new age where data is so abundant, our task as a civilization now is effective beneficial utilization. The challenge now is doing good things with that data - things that make our lives and the lives of future generations of people more fulfilling and more joyful and more prosperous.

If you have no idea whether the problem is with your product, price, or something else, then it’s a good idea to start with a little research. It’s going to help you understand what the main problem is, or why people are not buying more of your products.

So it is with statistics; no amount of fancy analysis can make up for fundamentally flawed data. Hence the expression “garbage in, garbage out.

Use the data from the clients you already have to help you find new clients just like them.

Use the data from the clients you already have to help you find new clients just like them. Eventually you'll be tapped into a whole market segment.

An analysis of G-rated (suitable for children) films released between 1990 and 2005 found that only 28% of speaking roles went to female characters – and perhaps even more tellingly in the context of humans being male by default, women made up only 17% of crowd scenes.

Hobbes's analysis of the causes of violence, borne out by modern data on crime and war, shows that violence is not a primitive, irrational urge, nor is it a "pathology" except in the metaphorical sense of a condition that everyone would like to eliminate. Instead, it is a near-inevitable outcome of the dynamics of self-interested, rational social organisms.

Your relevance as a data custodian is your ability to analyse and interpret it. If you can’t, your replacement is due.

Regression analysis is the hydrogen bomb of the statistics arsenal.

Data is the blood of any organization; coming from everywhere, used everywhere, connecting all the body, transferring messages and when analyzed it reflects the whole picture of the body.

Many modern businesses have become proficient at mining data. In fact the mining of data is becoming almost routine. But as we advance further into the 21rst century and the 22nd century, the utilization of data begins to take priority. So it's not just about collecting all this data, but also about getting really creative with generating new ways to utilize that data in the quest to add value.

Signals always point to something. In this sense, a signal is not a thing but a relationship. Data becomes useful knowledge of something that matters when it builds a bridge between a question and an answer. This connection is the signal.

Fear of this uncertainty motivates people to spin their wheels for days considering all the possible outcomes, calculating them in a spreadsheet using utility cost analysis or some other fancy method that even the guy who invented it doesn't use. But all that analysis just keeps you on the sidelines. Often you're better off flipping a coin and moving in any clear direction. Once you start moving, you get new data regardless of where you're trying to go. And the new data makes the next decision and the next better than staying on the sidelines desperately trying to predict the future without that time machine.

We think that if we have made a clever and thoughtful argument, based on data and smart analysis, then people will change their minds. This isn’t true. If you want to change people’s behavior, you need to touch their hearts, not just win the argument.

Blogging, writing conventional articles, and being science consultant and pocket protector ninja to various web portals and TV programs, quite often trying to promote the penicillin of hard data to people who had no interest in being cured of their ignorance.

A dispassionate conceptual development of the typology of violence must by definition ignore its traumatic impact. Yet there is a sense in which a cold analysis of violence somehow reproduces and participates in its horror. A distinction needs to be made, as well, between (factual) truth and truthfulness: what renders a report of a raped woman (or any other narrative of a trauma) truthful is its very factual unreliability, its confusion, its inconsistency. If the victim were able to report on her painful and humiliating experience in a clear manner, with all the data arranged in a consistent order, this very quality would make us suspicious of its truth.

Individual data points are of miniscule value. In the first twenty years of this century, data has become a common commodity. But the next level is amalgamation - bringing hundreds or thousands or millions of data points together and then making of them something greater than the sum of the parts.

All good decisions are Data dependent. To make good decisions, you need good data. And you need that good data to be organized according to it's applicable use value. So every business should be mining data and organizing data to enable business leaders to make good decisions on behalf of the business.

Here is one of the most important things to remember when doing research that involves regression analysis: Try not to kill anyone. You can even put a little Post-it note on your computer monitor: “Do not kill people with your research.” Because some very smart people have inadvertently violated that rule.

Investors look at economic fundamentals; traders look at each other; ‘quants’ look at the data. Dealing on the basis of historic price series was once described as technical analysis, or chartism (and there are chartists still). These savants identify visual patterns in charts of price data, often favouring them with arresting names such as ‘head and shoulders’ or ‘double bottoms’. This is pseudo-scientific bunk, the financial equivalent of astrology. But more sophisticated quantitative methods have since proved profitable for some since the 1970s’ creation of derivative markets and the related mathematics. Profitable

Liberals are more likely to see people as victims of circumstance and oppression, and doubt whether individuals can climb without governmental help. My own analysis using 2005 survey data from Syracuse University shows that about 90 percent of conservatives agree that “While people may begin with different opportunities, hard work and perseverance can usually overcome those disadvantages.” Liberals — even upper-income liberals — are a third less likely to say this.

The more time I spent in Finland, the more I started to worry that the reforms sweeping across the United States had the equation backwards. We were trying to reverse engineer a high-performance teaching culture through dazzlingly complex performance evaluations and value-added data analysis. It made sense to reward, train, and dismiss more teachers based on their performance, but that approach assumed that the worst teachers would be replaced with much better ones, and that the mediocre teachers would improve enough to give students the kind of education they deserved. However, there was not much evidence that either scenario was happening in reality.

The information he did not get was formal information. The data. The details. The options. The analysis. He didn’t do PowerPoint. For anything that smacked of a classroom or of being lectured to—“professor” was one of his bad words, and he was proud of never going to class, never buying a textbook, never taking a note—he got up and left the room.

All revolutions are impossible till they happen, then they become inevitable.

Most people use statistics the way a drunkard uses a lamp post, more for support than illumination.

One way to appreciate the brilliance of this acquisition is to look at Instagram’s “Power Index,” the number of people a platform reaches times their level of engagement. This social index reveals Instagram as the world’s most powerful platform, as it has 400 million users, a third of Facebook’s, but garners fifteen times the level of engagement. L2 Analysis of Unmetric Data.

No data are excluded on subjective or arbitrary grounds. No one piece of data is more highly valued than another. The consequences of this policy have to be accepted, even if they prove awkward.

In a way, science might be described as paranoid thinking applied to Nature: we are looking for natural conspiracies, for connections among apparently disparate data. Our objective is to abstract patterns from Nature (right-hemisphere thinking), but many proposed patterns do not in fact correspond to the data. Thus all proposed patterns must be subjected to the sieve of critical analysis (left-hemisphere thinking).

The success of college towns and big cities is striking when you just look at the data. But I also delved more deeply to undertake a more sophisticated empirical analysis. Doing so showed that there was another variable that was a strong predictor of a person’s securing an entry in Wikipedia: the proportion of immigrants in your county of birth. The greater the percentage of foreign-born residents in an area, the higher the proportion of children born there who go on to notable success. (Take that, Donald Trump!) If two places have similar urban and college populations, the one with more immigrants will produce more prominent Americans. What

Everything points to the same conclusion: that Twitter hasn’t so much altered our writing as just gotten it to fit into a smaller place. Looking through the data, instead of a wasteland of cut stumps, we find a forest of bonsai. This kind of in-depth analysis (lexical density, word frequency) hints at the real nature of the transformation under way. The change Twitter has wrought on language itself is nothing compared with the change it is bringing to the study of language. Twitter gives us a sense of words not only as the building blocks of thought but as a social connector, which indeed has been the purpose of language since humanity hunched its way across the Serengeti.

Take the issue of women being interrupted. An analysis of fifteen years of Supreme Court oral arguments found that ‘men interrupt more than women, and they particularly interrupt women more than they interrupt other men’.73 This goes for male lawyers (female lawyers weren’t found to interrupt at all) as well as judges, even though lawyers are meant to stop speaking when a justice starts speaking. And, as in the political sphere, the problem seems to have got worse as female representation on the bench has increased. An individualist solution might be to tell women to interrupt right back74 – perhaps working on their ‘polite interrupting’75 skills. But there’s a problem with this apparently gender-neutral approach, which is that it isn’t gender-neutral in effect: interrupting simply isn’t viewed the same way when women do it. In June 2017 US Senator Kamala Harris was asking an evasive Attorney General Jeff Sessions some tough questions. When he prevaricated once too often, she interrupted him and pressed him to answer. She was then in turn (on two separate occasions) interrupted and admonished by Senator John McCain for her questioning style.76 He did not do the same to her colleague Senator Rob Wyden, who subjected Sessions to similarly dogged questioning, and it was only Harris who was later dubbed ‘hysterical’.

Democrats need to catch up and leapfrog ahead. And this isn’t just about data. We need an “always-on” content distribution network that can match what the right-wing has built. That means an array of loosely connected Facebook pages, Instagram accounts, Twitter feeds, Snapchat stories, and Reddit communities churning out memes, graphics, and videos. More sophisticated data collection and analysis can support and feed this network. I’m no expert in these matters, but I know enough to understand that most people get their news from screens, so we have to be there 24/7.

Ron Rivest, one of the inventors of RSA, thinks that restricting cryptography would be foolhardy: It is poor policy to clamp down indiscriminately on a technology just because some criminals might be able to use it to their advantage. For example, any U.S. citizen can freely buy a pair of gloves, even though a burglar might use them to ransack a house without leaving fingerprints. Cryptography is a data-protection technology, just as gloves are a hand-protection technology. Cryptography protects data from hackers, corporate spies, and con artists, whereas gloves protect hands from cuts, scrapes, heat, cold, and infection. The former can frustrate FBI wiretapping, and the latter can thwart FBI fingerprint analysis. Cryptography and gloves are both dirt-cheap and widely available. In fact, you can download good cryptographic software from the Internet for less than the price of a good pair of gloves.

Dr. Helen Fisher divides love into three categories that correspond to different hormones and brain systems. Her analysis of the data suggests that high androgen and estrogen levels generate lust, romantic love correlates with high dopamine and norepinephrine and low serotonin, and attachment is driven by oxytocin and vasopressin. To make matters more complicated, these three systems interact. For example, testosterone can “kickstart the two love neurotransmitters while an orgasm can elevate the attachment hormone,” according to Fisher. “Don’t copulate with people you don’t want to fall in love with,” she warns.4

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

That so far the material has been dealt with in a rather subjective way provokes the question whether a means can be found of handling it objectively. [...] This chapter considers the applicability of the statistical tests employed by Wilson and the general problem whether the Linear B data are suited to statistical analysis.

As a thought experiment, von Neumann's analysis was simplicity itself. He was saying that the genetic material of any self-reproducing system, whether natural or artificial, must function very much like a stored program in a computer: on the one hand, it had to serve as live, executable machine code, a kind of algorithm that could be carried out to guide the construction of the system's offspring; on the other hand, it had to serve as passive data, a description that could be duplicated and passed along to the offspring. As a scientific prediction, that same analysis was breathtaking: in 1953, when James Watson and Francis Crick finally determined the molecular structure of DNA, it would fulfill von Neumann's two requirements exactly. As a genetic program, DNA encodes the instructions for making all the enzymes and structural proteins that the cell needs in order to function. And as a repository of genetic data, the DNA double helix unwinds and makes a copy of itself every time the cell divides in two. Nature thus built the dual role of the genetic material into the structure of the DNA molecule itself.

The display, which was called 'Can Democracy Survive the Internet?' was dedicated to a 'global election management' company called Cambridge Analytica. Cambridge Analytica claimed to have gathered 5,000 data points on every American voter online: what you liked and what you shared on social media; how and where you shopped; who your friends were... They claimed to be able to take this imprint of your online self, use it to understand your deepest drives and desires, and then draw on that analysis to change your voting behaviour. The boast seemed to be backed up by success: Cambridge Analytica had worked on the victorious American presidential campaign of Donald Trump; it had also run successful campaigns for US Senator Ted Cruz (twice); and others all across Africa, Asia, the Caribbean, Latin America.

The results of decades of neurotransmitter-depletion studies point to one inescapable conclusion: low levels or serotonin, norepinephrine or dopamine do not cause depression. here is how the authors of the most complete meta-analysis of serotonin-depletion studies summarized the data: "Although previously the monoamine systems were considered to be responsible for the development of major depressive disorder (MDD), the available evidence to date does not support a direct causal relationship with MDD. There is no simple direct correlation of serotonin or norepinephrine levels in the brain and mood.' In other words, after a half-century of research, the chemical-imbalance hypothesis as promulgated by the drug companies that manufacture SSRIs and other antidepressants is not only with clear and consistent support, but has been disproved by experimental evidence.

I like to ensure that I have music and art all around me. My personal favorite is old maps. What I love about old maps is that they are both beautiful and imperfect. These imperfections represent that some of the most talented in history were still very wrong (early cartography was very difficult). As the majority of my work is analysis and advisory, I find it a valuable reminder that my knowledge is limited. No matter how much data or insight I have, I can never fully “map out” any business. Yet, despite the incompleteness of these early cartographers, so much was learned of the world. So much done and accomplished. Therefore, these maps, or art pieces, serve as something to inspire both humility and achievement. This simple environmental factor helps my productivity and the overall quality of my work. Again, it’s like adding positive dice to my hand that are rolled each day.

If we had enough data then this statistical approach would undoubtedly sort out these things, and a lot of problems are arising precisely because we haven't got enough documents for the statistical approach to be wholly valid. I know you can calculate levels of probability and so forth, but to establish this really clearly we want a lot more information than we have actually got available. This is surely our major problem that we are still at the very limits at which you can use a technique of this sort. - John Chadwick

Many researchers have sought the secret of successful education by identifying the most successful schools in the hope of discovering what distinguishes them from others. One of the conclusions of this research is that the most successful schools, on average, are small. In a survey of 1,662 schools in Pennsylvania, for instance, 6 of the top 50 were small, which is an overrepresentation by a factor of 4. These data encouraged the Gates Foundation to make a substantial investment in the creation of small schools, sometimes by splitting large schools into smaller units. At least half a dozen other prominent institutions, such as the Annenberg Foundation and the Pew Charitable Trust, joined the effort, as did the U.S. Department of Education’s Smaller Learning Communities Program. This probably makes intuitive sense to you. It is easy to construct a causal story that explains how small schools are able to provide superior education and thus produce high-achieving scholars by giving them more personal attention and encouragement than they could get in larger schools. Unfortunately, the causal analysis is pointless because the facts are wrong. If the statisticians who reported to the Gates Foundation had asked about the characteristics of the worst schools, they would have found that bad schools also tend to be smaller than average. The truth is that small schools are not better on average; they are simply more variable. If anything, say Wainer and Zwerling, large schools tend to produce better results, especially in higher grades where a variety of curricular options is valuable. Thanks to recent advances in cognitive psychology,

The general laws of migration hold that the greater the obstacles and the farther the distance traveled, the more ambitious the migrants. “It is the higher status segments of a population which are most residentially mobile,” the sociologists Karl and Alma Taeuber wrote in a 1965 analysis of census data on the migrants, published the same year as the Moynihan Report. “As the distance of migration increases,” wrote the migration scholar Everett Lee, “the migrants become an increasingly superior group.” Any migration takes some measure of energy, planning, and forethought. It requires not only the desire for something better but the willingness to act on that desire to achieve it. Thus the people who undertake such a journey are more likely to be either among the better educated of their homes of origin or those most motivated to make it in the New World, researchers have found. “Migrants who overcome a considerable set of intervening obstacles do so for compelling reasons, and such migrations are not taken lightly,” Lee wrote. “Intervening obstacles serve to weed out some of the weak or the incapable.” The

Analysis of your social network and its members can also be highly revealing of your life, politics, and even sexual orientation, as demonstrated in a study carried out at MIT. In an analysis known as Gaydar, researchers studied the Facebook profiles of fifteen hundred students at the university, including those whose profile sexual orientation was either blank or listed as heterosexual. Based on prior research that showed gay men have more friends who are also gay (not surprising), the MIT investigators had a valuable data point to review the friend associations of their fifteen hundred students. As a result, researchers were able to predict with 78 percent accuracy whether or not a student was gay. At least ten individuals who had not previously identified as gay were flagged by the researchers’ algorithm and confirmed via in-person interviews with the students. While these findings might not be troubling in liberal Cambridge, Massachusetts, they could prove problematic in the seventy-six countries where homosexuality remains illegal, such as Sudan, Iran, Yemen, Nigeria, and Saudi Arabia, where such an “offense” is punished by death.

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.

In 2010, the dominance of inclusive fitness theory was finally broken. After struggling as a member of the small but still muted contrarian school for a decade, I joined two Harvard mathematicians and theoretical biologists, Martin Nowak and Corina Tarnita, for a top-to-bottom analysis of inclusive fitness. Nowak and Tarnita had independently discovered that the foundational assumptions of inclusive fitness theory were unsound, while I had demonstrated that the field data used to support the theory could be explained equally well, or better, with direct natural selection—as in the sex-allocation case of ants just described. Our joint report was published on August 26, 2010, as the cover article of the prestigious journal Nature. Knowing the controversy involved, the Nature editors had proceeded with unusual caution. One of them familiar with the subject and the mode of mathematical analysis came from London to Harvard to hold a special meeting with Nowak, Tarnita, and myself. He approved, and the manuscript was next examined by three anonymous experts. Its appearance, as we expected, caused a Vesuvian explosion of protest—the kind cherished by journalists. No fewer than 137 biologists committed to inclusive fitness theory in their research or teaching signed a protest in a Nature article published the following year. When I repeated part of my argument as a chapter in the 2012 book The Social Conquest of Earth, Richard Dawkins responded with the indignant fervor of a true believer. In his review for the British magazine Prospect, he urged others not to read what I had written, but instead to cast the entire book away, “with great force,” no less.

The largest and most rigorous study that is currently available in this area is the third one commissioned by the British Home Office (Kelly, Lovett, & Regan, 2005). The analysis was based on the 2,643 sexual assault cases (where the outcome was known) that were reported to British police over a 15-year period of time. Of these, 8% were classified by the police department as false reports. Yet the researchers noted that some of these classifications were based simply on the personal judgments of the police investigators, based on the victim’s mental illness, inconsistent statements, drinking or drug use. These classifications were thus made in violation of the explicit policies of their own police agencies. There searchers therefore supplemented the information contained in the police files by collecting many different types of additional data, including: reports from forensic examiners, questionnaires completed by police investigators, interviews with victims and victim service providers, and content analyses of the statements made by victims and witnesses. They then proceeded to evaluate each case using the official criteria for establishing a false allegation, which was that there must be either “a clear and credible admission by the complainant” or “strong evidential grounds” (Kelly, Lovett, & Regan,2005). On the basis of this analysis, the percentage of false reports dropped to 2.5%." Lonsway, Kimberly A., Joanne Archambault, and David Lisak. "False reports: Moving beyond the issue to successfully investigate and prosecute non-stranger sexual assault." The Voice 3.1 (2009): 1-11.

Two observations take us across the finish line. The Second Law ensures that entropy increases throughout the entire process, and so the information hidden within the hard drives, Kindles, old-fashioned paper books, and everything else you packed into the region is less than that hidden in the black hole. From the results of Bekenstein and Hawking, we know that the black hole's hidden information content is given by the area of its event horizon. Moreover, because you were careful not to overspill the original region of space, the black hole's event horizon coincides with the region's boundary, so the black hole's entropy equals the area of this surrounding surface. We thus learn an important lesson. The amount of information contained within a region of space, stored in any objects of any design, is always less than the area of the surface that surrounds the region (measured in square Planck units). This is the conclusion we've been chasing. Notice that although black holes are central to the reasoning, the analysis applies to any region of space, whether or not a black hole is actually present. If you max out a region's storage capacity, you'll create a black hole, but as long as you stay under the limit, no black hole will form. I hasten to add that in any practical sense, the information storage limit is of no concern. Compared with today's rudimentary storage devices, the potential storage capacity on the surface of a spatial region is humongous. A stack of five off-the-shelf terabyte hard drives fits comfortable within a sphere of radius 50 centimeters, whose surface is covered by about 10^70 Planck cells. The surface's storage capacity is thus about 10^70 bits, which is about a billion, trillion, trillion, trillion, trillion terabytes, and so enormously exceeds anything you can buy. No one in Silicon Valley cares much about these theoretical constraints. Yet as a guide to how the universe works, the storage limitations are telling. Think of any region of space, such as the room in which I'm writing or the one in which you're reading. Take a Wheelerian perspective and imagine that whatever happens in the region amounts to information processing-information regarding how things are right now is transformed by the laws of physics into information regarding how they will be in a second or a minute or an hour. Since the physical processes we witness, as well as those by which we're governed, seemingly take place within the region, it's natural to expect that the information those processes carry is also found within the region. But the results just derived suggest an alternative view. For black holes, we found that the link between information and surface area goes beyond mere numerical accounting; there's a concrete sense in which information is stored on their surfaces. Susskind and 'tHooft stressed that the lesson should be general: since the information required to describe physical phenomena within any given region of space can be fully encoded by data on a surface that surrounds the region, then there's reason to think that the surface is where the fundamental physical processes actually happen. Our familiar three-dimensional reality, these bold thinkers suggested, would then be likened to a holographic projection of those distant two-dimensional physical processes. If this line of reasoning is correct, then there are physical processes taking place on some distant surface that, much like a puppeteer pulls strings, are fully linked to the processes taking place in my fingers, arms, and brain as I type these words at my desk. Our experiences here, and that distant reality there, would form the most interlocked of parallel worlds. Phenomena in the two-I'll call them Holographic Parallel Universes-would be so fully joined that their respective evolutions would be as connected as me and my shadow.

A famous British writer is revealed to be the author of an obscure mystery novel. An immigrant is granted asylum when authorities verify he wrote anonymous articles critical of his home country. And a man is convicted of murder when he’s connected to messages painted at the crime scene. The common element in these seemingly disparate cases is “forensic linguistics”—an investigative technique that helps experts determine authorship by identifying quirks in a writer’s style. Advances in computer technology can now parse text with ever-finer accuracy. Consider the recent outing of Harry Potter author J.K. Rowling as the writer of The Cuckoo’s Calling , a crime novel she published under the pen name Robert Galbraith. England’s Sunday Times , responding to an anonymous tip that Rowling was the book’s real author, hired Duquesne University’s Patrick Juola to analyze the text of Cuckoo , using software that he had spent over a decade refining. One of Juola’s tests examined sequences of adjacent words, while another zoomed in on sequences of characters; a third test tallied the most common words, while a fourth examined the author’s preference for long or short words. Juola wound up with a linguistic fingerprint—hard data on the author’s stylistic quirks. He then ran the same tests on four other books: The Casual Vacancy , Rowling’s first post-Harry Potter novel, plus three stylistically similar crime novels by other female writers. Juola concluded that Rowling was the most likely author of The Cuckoo’s Calling , since she was the only one whose writing style showed up as the closest or second-closest match in each of the tests. After consulting an Oxford linguist and receiving a concurring opinion, the newspaper confronted Rowling, who confessed. Juola completed his analysis in about half an hour. By contrast, in the early 1960s, it had taken a team of two statisticians—using what was then a state-of-the-art, high-speed computer at MIT—three years to complete a project to reveal who wrote 12 unsigned Federalist Papers. Robert Leonard, who heads the forensic linguistics program at Hofstra University, has also made a career out of determining authorship. Certified to serve as an expert witness in 13 states, he has presented evidence in cases such as that of Christopher Coleman, who was arrested in 2009 for murdering his family in Waterloo, Illinois. Leonard testified that Coleman’s writing style matched threats spray-painted at his family’s home (photo, left). Coleman was convicted and is serving a life sentence. Since forensic linguists deal in probabilities, not certainties, it is all the more essential to further refine this field of study, experts say. “There have been cases where it was my impression that the evidence on which people were freed or convicted was iffy in one way or another,” says Edward Finegan, president of the International Association of Forensic Linguists. Vanderbilt law professor Edward Cheng, an expert on the reliability of forensic evidence, says that linguistic analysis is best used when only a handful of people could have written a given text. As forensic linguistics continues to make headlines, criminals may realize the importance of choosing their words carefully. And some worry that software also can be used to obscure distinctive written styles. “Anything that you can identify to analyze,” says Juola, “I can identify and try to hide.