Causation Correlation Quotes

One of the first things taught in introductory statistics textbooks is that correlation is not causation. It is also one of the first things forgotten.

Most of you will have heard the maxim "correlation does not imply causation." Just because two variables have a statistical relationship with each other does not mean that one is responsible for the other. For instance, ice cream sales and forest fires are correlated because both occur more often in the summer heat. But there is no causation; you don't light a patch of the Montana brush on fire when you buy a pint of Haagan-Dazs.

How much do you hate this, on a scale from one to ‘correlation equals causation’?

everyone assumes causation when they should be thinking coincidence, and correlation when they should be asking whether Twitter is really a reliable source of information.

My science teacher said that just because two things happened together didn't mean one was because of the other, or as she put it: correlation does not imply causation.

People are by nature illiterate and innumerate, quantifying the world by “one, two, many” and by rough guesstimates.21 They understand physical things as having hidden essences that obey the laws of sympathetic magic or voodoo rather than physics and biology: objects can reach across time and space to affect things that resemble them or that had been in contact with them in the past (remember the beliefs of pre–Scientific Revolution Englishmen).22 They think that words and thoughts can impinge on the physical world in prayers and curses. They underestimate the prevalence of coincidence.23 They generalize from paltry samples, namely their own experience, and they reason by stereotype, projecting the typical traits of a group onto any individual that belongs to it. They infer causation from correlation. They think holistically, in black and white, and physically, treating abstract networks as concrete stuff. They are not so much intuitive scientists as intuitive lawyers and politicians, marshaling evidence that confirms their convictions while dismissing evidence that contradicts them.24 They overestimate their own knowledge, understanding, rectitude, competence, and luck.25

There is a difference between correlation and causation - many people mistake one for the other.

How much do you hate this, on a scale of one to 'correlation equals causation'.

Abigail gave me a withering look. But everyone assumes causation when they should be thinking coincidence, and correlation when they should be asking whether Twitter is really a reliable source of information.

Just as citizens should grasp the basics of history, science, and the written word, they should command the intellectual tools of sound reasoning. These include logic, critical thinking, probability, correlation and causation, the optimal ways to adjust our beliefs and commit to decisions with uncertain evidence, and the yardsticks for making rational choices alone and with others.

It’s an iron rule of mine that I never argue correlation versus causation in the middle of the night, especially when I had an alternative option.

Correlation doesn't imply causation - but apparently it doesn't sell newspapers either.

More often than not correlation is mistaken for causation.

One of the first things taught in introductory statistics textbooks is that correlation is not causation. It is also one of the first things forgotten

For example, we have been told that those of us who drink a moderate amount of alcohol tend to be in better health. That is a correlation. Does this mean drinking a moderate amount will improve one’s health—a causation? Perhaps not. It could be that good health causes people to drink a moderate amount. Social scientists call this reverse causation. Or it could be that there is an independent factor that causes both moderate drinking and good health. Perhaps spending a lot of time with friends leads to both moderate alcohol consumption and good health. Social scientists call this omitted-variable bias.

Perhaps the biggest misconception some managers may run into is the belief that correlation proves causation. The fact that one variable is correlated to another does not necessarily mean that one variable causes the other. If church donations and liquor sales are correlated, it is not because of some collusion between clergy and the liquor industry. It is because both are affected by how well the economy is doing.

Quantum fluctuations are, at their root, completely a-causal, in the sense that cause and effect and ordering of events in time is not a part of how these fluctuations work. Because of this, there seem not to be any correlations built into these kinds of fluctuations because 'law' as we understand the term requires some kind of cause-and-effect structure to pre-exist. Quantum fluctuations can precede physical law, but it seems that the converse is not true. So in the big bang, the establishment of 'law' came after the event itself, but of course even the concept of time and causality may not have been quite the same back then as they are now.

However, correlation does not necessarily imply causation. The fact that people tend to carry umbrellas when it rains creates a high correlation between umbrella carrying and rain showers. However, it is obvious that choosing to carry an umbrella does not cause rainfall.

Plenty more. A whole lotta Black women been getting sick, losing babies, and everything—and they blame it all on those Melancons. I don’t believe in correlation actually implying causation. But it does make you wonder who they are giving the caul to if not the women of this neighborhood.

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.

A classic illustration of this difficulty is that countries with more telephone poles often have a higher incidence of heart disease, and many other diseases. Therefore, telephone poles and heart disease are positively correlated. But this does not prove that telephone poles cause heart disease. In effect, correlation does not equal causation.

Science has no connection with Truth. It’s simply a pattern-matching exercise, i.e. it tries to match theoretical mathematical patterns to experimentally observed patterns, and, when it makes a match, it believes it has made some kind of discovery about reality. It has done no such thing. Correlation is not causation! Everyone know that, but apparently not scientists!

Distinguishing between correlation and causation is critical to our understanding of the biology and conservation of monarchs and milkweeds. Turning back to our study of chocolate: countrywide spending on science also correlates with per capita income, the latter of which correlates with chocolate consumption (at least in the Western world). Even so, I would happily participate in a controlled study to determine the influence of chocolate consumption on scientific discoveries.

As a part of their effort to turn Watson into a practical tool, IBM researchers confronted one of the primary tenets of the big data revolution: the idea that prediction based on correlation is sufficient, and that a deep understanding of causation is usually both unachievable and unnecessary. A new feature they named “WatsonPaths” goes beyond simply providing an answer and lets researchers see the specific sources Watson consulted, the logic it used in its evaluation, and the inferences it made on

I bump into a group of girls congregating around a locker. Jessica, Willow (who is notably the only Willow enrolled in our 397-student class and in our 1,579-student school), and Abby. Miney has labeled them in my notebook, in block letters and underlined with a Sharpie:THE POPULAR BITCHES. When she first used this designation, Miney had to give me a long lecture about how this wasn’t an oxymoron, how someone could be both popular, which I presumed meant that lots of people liked you, and at the same time also be a bitch, which I presumed would have the opposite outcome. Apparently popularity in the context of high school has a negative correlation with people actually liking you but a high correlation with people wanting to be your friend. After careful consideration, this makes sense, though in my case, I am both an outlier and a great example of the fact that correlation does not imply causation. I am nice to everyone but without any upside: People neither like me nor want to be my friend.

On the other hand, maybe what attracts us aren't the stories of falling apart so much as the stories of self-creation. The falling apart stuff is just a byproduct, a hazard of the trade. Maybe what I loved about Camille Claudel was what she created out of what she smashed to bits. How did a bourgeois girl become an artist and a woman? What was the female equivalent of the Great Man? If it didn't exist, why not? Who said it didn't? Who said it couldn't? What were the conditions that made it so hard? Rodin was the image Claudel identified with and against which she defined herself. Scott was this image for Zelda. A woman could not be a great artist and have a traditional marriage - not unless her husband was a Leonard Woolf. One boyfriend I had in college used to joke, 'Only one artist in the family,' meaning not me. I didn't get it then, but I get it now. There was always something self-annihilating in the act of loving, for a girl with creative aspirations - always - but far more then than now. The message, invariably, was that youthful passions lead to middle-age breakdowns, so choose your institution wisely. Marriage or the nuthouse. One or the other. It started to dawn on me that it wasn't that I was attracted to stories about girls who went mad, I was attracted to stories about girls with ambitions who wound up institutionalized. Getting locked up was not the result of adventure, it was the price you paid for adventure, it was your punishment. I had mistaken correlation for causation. Rookie mistake.

In explaining this Somalia-to-Sweden continuum, with poor violent repressive unhappy countries at one end and rich peaceful liberal happy ones at the other, correlation is not causation, and other factors like education, geography, history, and culture may play roles.60 But when the quants try to tease them apart, they find that economic development does seem to be a major mover of human welfare.61 In an old academic joke, a dean is presiding over a faculty meeting when a genie appears and offers him one of three wishes—money, fame, or wisdom. The dean replies, “That’s easy. I’m a scholar. I’ve devoted my life to understanding. Of course I’ll

Network theory confirms the view that information can take on 'a life of its own'. In the yeast network my colleagues found that 40 per cent of node pairs that are correlated via information transfer are not in fact physically connected; there is no direct chemical interaction. Conversely, about 35 per cent of node pairs transfer no information between them even though they are causally connected via a 'chemical wire' (edge). Patterns of information traversing the system may appear to be flowing down the 'wires' (along the edges of the graph) even when they are not. For some reason, 'correlation without causation' seems to be amplified in the biological case relative to random networks.

If we’re not careful, the automation of mental labor, by changing the nature and focus of intellectual endeavor, may end up eroding one of the foundations of culture itself: our desire to understand the world. Predictive algorithms may be supernaturally skilled at discovering correlations, but they’re indifferent to the underlying causes of traits and phenomena. Yet it’s the deciphering of causation—the meticulous untangling of how and why things work the way they do—that extends the reach of human understanding and ultimately gives meaning to our search for knowledge. If we come to see automated calculations of probability as sufficient for our professional and social purposes, we risk losing or at least weakening our desire and motivation to seek explanations, to venture down the circuitous paths that lead toward wisdom and wonder. Why bother, if a computer can spit out “the answer” in a millisecond or two?

People are by nature illiterate and innumerate, quantifying the world by “one, two, many” and by rough guesstimates.21 They understand physical things as having hidden essences that obey the laws of sympathetic magic or voodoo rather than physics and biology: objects can reach across time and space to affect things that resemble them or that had been in contact with them in the past (remember the beliefs of pre–Scientific Revolution Englishmen).22 They think that words and thoughts can impinge on the physical world in prayers and curses. They underestimate the prevalence of coincidence.23 They generalize from paltry samples, namely their own experience, and they reason by stereotype, projecting the typical traits of a group onto any individual that belongs to it. They infer causation from correlation. They think holistically, in black and white, and physically, treating abstract networks as concrete stuff. They are not so much intuitive scientists as intuitive lawyers and politicians, marshaling evidence that confirms their convictions while dismissing evidence that contradicts them.24 They overestimate their own knowledge, understanding, rectitude, competence, and luck.25 The

If we’re not careful, the automation of mental labor, by changing the nature and focus of intellectual endeavor, may end up eroding one of the foundations of culture itself: our desire to understand the world. Predictive algorithms may be supernaturally skilled at discovering correlations, but they’re indifferent to the underlying causes of traits and phenomena. Yet it’s the deciphering of causation—the meticulous untangling of how and why things work the way they do—that extends the reach of human understanding and ultimately gives meaning to our search for knowledge. If we come to see automated calculations of probability as sufficient for our professional and social purposes, we risk losing or at least weakening our desire and motivation to seek explanations, to venture down the circuitous paths that lead toward wisdom and wonder. Why bother, if a computer can spit out “the answer” in a millisecond or two? In his 1947 essay “Rationalism in Politics,” the British philosopher Michael Oakeshott provided a vivid description of the modern rationalist: “His mind has no atmosphere, no changes of season and temperature; his intellectual processes, so far as possible, are insulated from all external influence and go on in the void.” The rationalist has no concern for culture or history; he neither cultivates nor displays a personal perspective. His thinking is notable only for “the rapidity with which he reduces the tangle and variety of experience” into “a formula.”54 Oakeshott’s words also provide us with a perfect description of computer intelligence: eminently practical and productive and entirely lacking in curiosity,

реальность бывает очень жестока с теми, кто теряет с ней связь Correlation does not imply causation Концепция «единого психоза» возникла еще в середине XIX в. и до сих пор остается неопровергнутой. По происхождению расстройства тоже делятся на три типа: экзогенные, эндогенные и психогенные. Экзогенные, как можно догадаться из названия, вызваны внешними причинами – в эту группу попадают: • аддикции (навязчивая потребность в определенном действии, таком как участие в азартных играх или употребление психоактивных веществ); • психозы, вызванные злоупотреблением разными веществами («белая горячка» или галлюцинации в виде «жучков», из-за которых наркоманы, сидящие на кокаине или метамфетамине, расковыривают себе руки); • последствия черепно-мозговых травм, самый яркий пример такого экзогенного расстройства – знаменитая история Финнеаса Гейджа, дорожного рабочего, которому случайно пробило голову железным штырем. Гейдж выжил и даже выздоровел, но повреждение определенных участков мозга привело к необратимым изменениям личности: прежде уравновешенный мужчина стал вести себя непредсказуемо и агрессивно; • расстройства, являющиеся побочными эффектами непсихических заболеваний, так, вирусная пневмония может усугубляться галлюцинациями, а при инфекционном гепатите развиваются депрессии и истерические расстройства. Эндогенные заболевания вызваны внутренними причинами, чаще всего связанными с наследственностью, – дисфункциями определенных участков мозга и нарушениями в нейронах. Скажем, сбивается механизм обмена информацией между нейронами с помощью нейромедиаторов. К подобным болезням относят шизофрению, эпилепсию и биполярное расстройство. Психогенные заболевания – расстройства, обусловленные психологическими причинами: посттравматический синдром или депрессия, возникшие на фоне потери любимого человека. в конце концов, счастье – это тоже скорее аномалия. Многим кажется, что постоянная удовлетворенность жизнью – нормальное и желательное положение дел. Но такое ли это типичное состояние для психики, как мы привыкли думать? Профессор Ричард Бенталл из Ливерпульского университета в 1992 г. опубликовал провокационную статью, в которой утверждал, что счастье стоит отнести в разряд психических расстройств под названием «большое аффективное расстройство приятного типа». Профессор последовательно доказывает, что счастье – статистически нетипичное состояние с рядом определенных симптомов, ассоциируется со спектром когнитивных нарушений и вообще связано с ненормальным функционированием центральной нервной системы. Так что единственный довод против включения счастья в диагностические справочники – на него никто не жалуется. «Но с научной точки зрения это нерелевантный критерий», – заключает Бенталл. Ученые из Пизанского университета выяснили, что при сильной влюбленности некоторые отделы мозга активизируются так же, как и при обсессивно-компульсивном расстройстве Мозг (по мнению самого мозга) – самый важный орган в нашем теле, он лучше всех защищен и сложнее устроен.

The dilemma is, as it is often said, correlation does not imply causation.5 The discovery of a predictive relationship between A and B does not mean one causes the other, not even indirectly. No way, nohow.

how difficult it is to ferret out causes; they do not confuse correlation (A occurs before B) with causation (A caused B);

I am now going to argue that neuroscience does not address, even less answer, the fundamental question of the relation(s) between matter and mind, body and mind, or brain and mind. If it seems to do so this is only the result of a confusion between, indeed a conflation of, three quite different relations: correlation, causation and identity.

The errors of muddling correlation with causation, necessary condition with sufficient causation, and sufficient causation with identity lie at the heart of the neuromaniac’s basic assumption that consciousness and nerve impulses are one and the same, and that (to echo a commonly used formulation) “the mind is a creation of the brain”.

Correlation doesn’t imply causation, but it does waggle its eyebrows suggestively and gesture furtively while mouthing, “Look over there.”[

Evolutionists are nothing but novelists, they abridge correlation into causation to establish a narrative of their own.

AI cannot capitalize on the expertise of millions of people. Everyday there are small things that we experience which have contextual significance and contribute to how we think and create. This includes how we feel, our mood, and others that are non-measureable by sensors. I might be "old school" but what is described as AI today is little more than correlation without causation. Remember that the algorithms that drive the automation are developed by data scientists. The systems that run the algorithms are neither artificial nor intelligent, despite being created by highly intelligent people (who are very real).

confusing correlation with causation.

But even generously granting the assumption that a correlation does exist between pornography and violence, what would such a correlation tell us? It would certainly not indicate a cause-and-effect relationship. It is a fallacy to assume that if A can be correlated with B, then A causes B. Such a correlation may indicate nothing more than that both are caused by another factor, C. For example, there is a high correlation between the number of doctors in a city and the number of alcoholics there. One doesn't cause the other; both statistics are proportional to the size of the city's population.

Instead, it’s likely that whether people are fixed or fluid influences both their political beliefs and their residential, occupational, educational, religious, and consumer choices. Worldview, in other words, underpins both political and nonpolitical preferences. It is important to note that, when we observe connections between partisanship and nonpolitical decisions, we are talking about correlation, not causation.

science.” If much of what sociology has to offer seems like common sense, in other words, it is not just because everything about human behavior seems obvious once you know the answer. Part of the problem is also that social scientists, like everyone else, participate in social life and so feel as if they can understand why people do what they do simply by thinking about it. It is not surprising, therefore, that many social scientific explanations suffer from the same weaknesses—ex post facto assertions of rationality, representative individuals, special people, and correlation substituting for causation—that pervade our commonsense explanations as well.

In that respect the germ theory he espoused required more robust proof. Only the further development of microscopy, which rendered the Vibrio cholerae visible, and the experimental method, which demonstrated the role of microorganisms in inducing disease in animals, could establish the actual mechanisms of contagion. Snow painstakingly established correlation, but he was unable to prove causation.

An issue with current data science methodologies is that the impact of contextual awareness is underestimated since the problem is much more complex. At times we incorrectly equate correlation with causation based on incomplete data or lack of understanding sensitive dependencies between data sets. - Tom Golway

Unleashing Reliable Insights from Generative AI by Disentangling Language Fluency and Knowledge Acquisition Generative AI carries immense potential but also comes with significant risks. One of these risks of Generative AI lies in its limited ability to identify misinformation and inaccuracies within the contextual framework. This deficiency can lead to mistakenly associating correlation with causation, reliance on incomplete or inaccurate data, and a lack of awareness regarding sensitive dependencies between information sets. With society’s increasing fascination with and dependence on Generative AI, there is a concern that the unintended consequence that it will have an unhealthy influence on shaping societal views on politics, culture, and science. Humans acquire language and communication skills from a diverse range of sources, including raw, unfiltered, and unstructured content. However, when it comes to knowledge acquisition, humans typically rely on transparent, trusted, and structured sources. In contrast, large language models (LLMs) such as ChatGPT draw from an array of opaque, unattested sources of raw, unfiltered, and unstructured content for language and communication training. LLMs treat this information as the absolute source of truth used in their responses. While this approach has demonstrated effectiveness in generating natural language, it also introduces inconsistencies and deficiencies in response integrity. While Generative AI can provide information it does not inherently yield knowledge. To unlock the true value of generative AI, it is crucial to disaggregate the process of language fluency training from the acquisition of knowledge used in responses. This disaggregation enables LLMs to not only generate coherent and fluent language but also deliver accurate and reliable information. However, in a culture that obsesses over information from self-proclaimed influencers and prioritizes virality over transparency and accuracy, distinguishing reliable information from misinformation and knowledge from ignorance has become increasingly challenging. This presents a significant obstacle for AI algorithms striving to provide accurate and trustworthy responses. Generative AI shows great promise, but addressing the issue of ensuring information integrity is crucial for ensuring accurate and reliable responses. By disaggregating language fluency training from knowledge acquisition, large language models can offer valuable insights. However, overcoming the prevailing challenges of identifying reliable information and distinguishing knowledge from ignorance remains a critical endeavour for advancing AI algorithms. It is essential to acknowledge that resolving this is an immediate challenge that needs open dialogue that includes a broad set of disciplines, not just technologists Technology alone cannot provide a complete solution.

figure out why those things happened, we are susceptible to a variety of cognitive traps, like assuming causation when there is only a correlation, or cherry-picking data to confirm the narrative we prefer. We will pound a lot of square pegs into round holes to maintain the illusion of a tight relationship between our outcomes and our decisions.

The danger of generative AI is that it lacks the ability to understand misinformation, leading to incorrectly equating correlation with causation based on incomplete/inaccurate data or lack of contextual awareness required to understand sensitive dependencies between data sets. The unintended consequence is technology shaping societal views on politics, culture and science.

Astrology Course 101: The Ultimate Guide for Beginners to Professional Level Astrology Course - You should approach astrology with an open mind and a critical viewpoint if you're interested in knowing more about it. Here are some tips to get you started learning about astrology: Know Its Nature: A belief system that has not been proven by science is astrology. The scientific community discredits it as a pseudoscience because none of its tenets are supported by empirical data. Consider astrology as a sort of entertainment or personal belief rather than as a science. Basic Knowledge: Learn the basic principles of astrology, such as the meanings of the zodiac signs, natal charts, planets, aspects, and houses, before moving on. There are numerous publications, websites, and educational programmes that can offer a solid foundation. Analyze Your Natal Chart: Your birth date, time, and location can be used to make your own natal chart. You may create your chart for free with the aid of several online tools and programmes. A map of the celestial bodies' positions at the moment of your birth is called your natal chart. Analyze Your Natal Chart: Spend some time studying your natal chart once you have one. Find out what each house's planets mean and how they interact with one another. Your personality, weaknesses, and prospective life path will all be revealed by this. Observe Your Sun, Moon, and Rising Signs by Reading This: Your Sunrise (or Ascendant) sign affects your outward behavior, while your Moon sign and Sun sign both reflect your emotional nature. A more complete understanding of these signs might give you a better understanding of your astrological profile. Consult with Expert Astrologers: Consider speaking with a qualified astrologer if you want a more thorough examination of your chart or if you have specific queries. Based on your chart, they can provide unique insights and interpretations. Use astrology to reflect on yourself: Astrology is a useful tool for introspection and personal development. Examine how your own experiences and emotions align with the astrological insights. Use it to gain a deeper understanding of who you are and the course of your life. Keep in Mind That Symbolism: Numerous people have called astrology a symbolic language. Astrologers interpret the locations and aspects of celestial bodies as symbols in their own unique ways. It does not accurately describe how the cosmos affects your life. Learn to Think Critically: Keep a critical mindset while you research astrology. Recognise that connections drawn by astrology lack scientific support and that correlation does not imply causation. Consider several points of view and be willing to be skeptical. Respect for Various Beliefs: Regarding astrology, people hold a variety of beliefs and practices. Even if they differ from your own, respect other people's decisions and ideas. For some people, astrology holds significant personal value. Science and balance: Astrology is not a replacement for critical thinking or decision-making based on evidence. Use more trustworthy information and logic while making crucial life decisions. In Conclusion - Astrology has the potential to be a fascinating and contemplative activity that provides insights into personality and self-awareness. But it's crucial to approach it from a well-informed and impartial standpoint, mindful of its limitations and cognizant that it is largely a belief system rather than a science. For More Details : Click Here

but do we know whether overparenting causes this rise in mental health problems? The answer is that we don’t have studies proving causation, but a number of recent studies show correlation. A study published in 2006 by UCLA clinical child psychologist and assistant professor of psychiatry and education, James Wood, found that parents who tend to take over tasks that children either are or could be performing independently limit the child’s ability to experience “mastery,” leading to greater rates of separation anxiety in their children.

How can this be squared with the above-mentioned psychological and sociological findings that, for example, married people are happier on average than singles? First, these findings are correlations – the direction of causation may be the opposite of what some researchers have assumed. It is true that married people are happier than singles and divorcees, but that does not necessarily mean that marriage produces happiness. It could be that happiness causes marriage. Or more correctly, that serotonin, dopamine and oxytocin bring about and maintain a marriage. People who are born with a cheerful biochemistry are generally happy and content. Such people are more attractive spouses, and consequently they have a greater chance of getting married. They are also less likely to divorce, because it is far easier to live with a happy and content spouse than with a depressed and dissatisfied one. Consequently, it’s true that married people are happier on average than singles, but a single woman prone to gloom because of her biochemistry would not necessarily become happier if she were to hook up with a husband.

These steps will help you remember that correlation is not causation, and that most market prophecies are based on coincidental patterns. That was the problem in the late 1990s at the Motley Fool website. Its Foolish Four portfolios were based on research claiming that factors like the ratio of a company’s dividend yield to the square root of its stock price could predict future outperformance. In the long run, however, a company’s stock can rise only if its underlying business earns more money.

correlation is not causation

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.

correlation and causation. It’s one thing to be linked to a disease; it’s quite another to cause a disease, which implies a directing, controlling action. If I show you my keys and say that a particular key “controls” my car, you at first might think that makes sense because you know you need that key to turn on the ignition. But does the key actually “control” the car? If it did, you couldn’t leave the key in the car alone because it might just borrow your car for a joy ride when you are not paying attention. In truth, the key is “correlated” with the control of the car; the person who turns the key actually controls the car. Specific genes are correlated with an organism’s behavior and characteristics. But these genes are not activated until something triggers them.

In strict logic, correlation and causation are entirely distinct, but the close temporal association of fire with the permanent end of the plague made this hypothesis irresistibly tempting.

Though they are essential, emotions affect judgement when left unchecked. This then leads to invalid assumptions and flawed perceptions. Facts are misconstrued, correlation is confused with causation and reality begins to depart a path of truth.

This distinction between correlation and causation is crucial to the proper interpretation of statistical results.

Correlation does not imply causation. Yet in many discussions in universities these days, the correlation of a demographic trait or identity group membership with an outcome gap is taken as evidence that discrimination (structural or individual) caused the outcome gap. Sometimes it did, sometimes it didn’t, but if people can’t raise alternative possible causal explanations without eliciting negative consequences, then the community is unlikely to arrive at an accurate understanding of the problem. And without understanding the true nature of a problem, there is little chance of solving it.

distinguish the mere correlates of consciousness from the genuine signatures of consciousness. Although the quest for the brain mechanisms of conscious experience is often described as a search for neural correlates of consciousness, this phrase is inadequate. Correlation is not causation, and a mere correlate is therefore insufficient. Too

Correlation does not imply causation,

In Science, Correlation does not imply Causation; while in Theology, Creation does not imply Causation.

So you’re saying that my entry camera has a glitch, which happened to show up exactly when you did, and which spontaneously resolved as soon as you were no longer in front of it?” He shrugs, but still can’t meet my eyes. “This is possible, is it not? Correlation does not prove causation.

If philosophy is regarded as a legitimate and necessary discipline, then one might think that a certain degree of philosophical training would be very useful to a scientist. Scientists ought to be able to recognize how often philosophical issues arise in their work — that is, issues that cannot be resolved by arguments that make recourse solely to inference and empirical observation. In most cases, these issues arise because practicing scientists, like all people, are prone to philosophical errors. To take an obvious example, scientists can be prone to errors of elementary logic, and these can often go undetected by the peer review process and have a major impact on the literature — for instance, confusing correlation and causation, or confusing implication with a biconditional. Philosophy can provide a way of understanding and correcting such errors. It addresses a largely distinct set of questions that natural science alone cannot answer, but that must be answered for natural science to be properly conducted. [The folly of scientism]

When we work backward from results to figure out why those things happened, we are susceptible to a variety of cognitive traps, like assuming causation when there is only a correlation, or cherry-picking data to confirm the narrative we prefer. We will pound a lot of square pegs into round holes to maintain the illusion of a tight relationship between our outcomes and our decisions.

confusing correlation with causation.4

correlation does not imply causation.

That need for surprise is what it’s all about. Surprise is visceral and immediate, and stokes our dopamine and our nucleus accumbens. But it’s fleeting, and rarely does any happiness come out of it. In fact, the frequent checking of cell phones, waiting for something to change, is linked to anxiety and depression.6 Of course, again, correlation is not causation. Do cell phones cause depression? Or are depressed people trying to eke out a little dopamine rush? Or both? I’ll tell you one thing: cell phones certainly don’t bring serenity.

But everyone assumes causation when they should be thinking coincidence, and correlation when they should be asking whether Twitter is really a reliable source of information.

Correlation is not causation,

that correlation does not show causation.

As Nate Silver, author of The Signal and the Noise: Why So Many Predictions Fail—But Some Don’t, points out, “ice cream sales and forest fires are correlated because both occur more often in the summer heat. But there is no causation; you don’t light a patch of the Montana brush on fire when you buy a pint of Häagen-Dazs.” Of course, it’s no surprise that correlation isn’t the same as causality. But although most organizations know that, I don’t think they act as if there is a difference. They’re comfortable with correlation. It allows managers to sleep at night. But correlation does not reveal the one thing that matters most in innovation—the causality behind why I might purchase a particular solution. Yet few innovators frame their primary challenge around the discovery of a cause. Instead, they focus on how they can make their products better, more profitable, or differentiated from the competition. As W. Edwards Deming, the father of the quality movement that transformed manufacturing, once said: “If you do not know how to ask the right question, you discover nothing.” After decades of watching great companies fail over and over again, I’ve come to the conclusion that there is, indeed, a better question to ask: What job did you hire that product to do? For me, this is a neat idea. When we buy a product, we essentially “hire” something to get a job done. If it does the job well, when we are confronted with the same job, we hire that same product again. And if the product does a crummy job, we “fire” it and look around for something else we might hire to solve the problem. Every day stuff happens to us. Jobs arise in our lives that we need to get done. Some jobs are little (“ pass the time while waiting in line”), some are big (“ find a more fulfilling career”). Some surface unpredictably (“ dress for an out-of-town business meeting after the airline lost my suitcase”), some regularly (“ pack a healthy, tasty lunch for my daughter to take to school”). Other times we know they’re coming. When we realize we have a job to do, we reach out and pull something into our lives to get the job done. I might, for example, choose to buy the New York Times because I have a job to fill my time while waiting for a doctor’s appointment and I don’t want to read the boring magazines available in the lobby. Or perhaps because I’m a basketball fan and it’s March Madness time. It’s only when a job arises in my life that the Times can solve for me that I’ll choose to hire the paper to do it. Or perhaps I have it delivered to my door so that my neighbors think I’m informed—and nothing about their ZIP code or median household income will tell the Times that either.

Correlation isn’t causation—

correlation does not imply causation to describe this fallacy.

But the most important thing to remember is that correlation does not show causation.