What is *How Not to Be Wrong* by Jordan Ellenberg about?

*How Not to Be Wrong* explores how mathematical thinking helps avoid errors in everyday decisions, blending real-world examples like survivorship bias in WWII aircraft armor and lottery ticket probabilities. Jordan Ellenberg translates complex concepts into accessible insights without advanced formulas, focusing on logic, problem-solving, and interpreting data wisely.

Who should read *How Not to Be Wrong*?

This book suits curious readers, professionals, and students seeking to sharpen critical thinking. It’s ideal for those intimidated by math but interested in its practical applications—from analyzing news headlines to making financial decisions. Ellenberg’s engaging style makes it accessible to non-experts while offering depth for math enthusiasts.

Is *How Not to Be Wrong* worth reading?

Yes—ranked a *New York Times* bestseller, the book bridges math and daily life through compelling narratives like the “dead fish MRI” experiment and election prediction errors. It’s praised for transforming abstract concepts into tools for clearer decision-making, making it valuable for readers across disciplines.

What is survivorship bias in *How Not to Be Wrong*?

Survivorship bias occurs when we focus only on observed outcomes (e.g., bullet holes on returning WWII planes) while ignoring hidden data (e.g., planes that didn’t survive). Ellenberg uses this to illustrate flawed reasoning and the importance of considering *all* data, not just visible successes.

How does Jordan Ellenberg explain mathematical intuition?

Ellenberg argues math formalizes common sense, like why 5 + 7 equals 7 + 5 (commutativity). He demystifies equations by showing they codify intuitive truths, helping readers trust their logic while avoiding oversimplification.

What career fields benefit from *How Not to Be Wrong*?

Data analysts, educators, policymakers, and entrepreneurs gain tools to interpret statistics, avoid cognitive traps, and communicate findings effectively. Ellenberg emphasizes aligning mathematical models with audience understanding—a key skill in analytics and decision-heavy roles.

How does *How Not to Be Wrong* critique misapplied statistics?

The book highlights cases like flawed teacher assessments or misleading medical studies, showing how misusing averages or ignoring context leads to errors. Ellenberg stresses questioning assumptions and validating methodologies before trusting results.

What real-life examples does Ellenberg use to teach math?

- **Lottery tickets**: Calculating expected value vs. emotional payoff. - **Fish MRI study**: Exposing flawed statistical significance in brain scans. - **Election predictions**: How linear regression misleads without nuance.

How not to be wrong: Die Kunst des mathematischen Denkens

Die verborgene Mathematik des Alltags

Jordan Ellenberg

3.95(20963 Reviews)

Überblick über How not to be wrong: Die Kunst des mathematischen Denkens

Entdecken Sie, warum Bill Gates diesen Bestseller als „einen Liebesbrief an die Mathematik“ bezeichnete. Ellenberg verwandelt mathematisches Denken in eine Superkraft für den Alltag und zeigt auf, wie Abraham Walds kontraintuitive Lösung für die Panzerung von Flugzeugen im Zweiten Weltkrieg die verborgene Brillanz verdeutlicht, die darin liegt, bessere Fragen zu stellen.

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Kapitel 1

When Numbers Reveal the Hidden Structure of Reality

Have you ever dismissed mathematics as irrelevant to your daily life? Perhaps you've uttered those familiar words: "When am I ever going to use this?" Jordan Ellenberg's "How Not to Be Wrong" demolishes this misconception with startling clarity. Mathematics isn't about memorizing formulas-it's about developing X-ray vision that reveals the hidden structures beneath our chaotic world. This New York Times bestseller has been praised by Bill Gates as "the book that showed me how math underpins our daily lives," and has transformed how thousands approach everyday reasoning. Ellenberg, a professor at the University of Wisconsin-Madison with a rare gift for making complex ideas accessible, shows us that math isn't just a set of rules but an extension of common sense-a powerful tool that helps us avoid being wrong about things that matter.

Kapitel 2

The Missing Bullet Holes: Seeing What Isn't There

During World War II, the U.S. military faced a critical question: where should they add armor to their aircraft? Officers observed that returning planes had more bullet holes in their fuselage than in their engines. The obvious conclusion seemed to be to reinforce the most-hit areas. But mathematician Abraham Wald recognized something profound that others missed-they were only examining the planes that made it back. The planes hit in the engine weren't returning at all. The missing bullet holes told the real story. Wald's insight was that armor should protect the parts that, when hit, prevent planes from returning. This counterintuitive approach saved countless aircraft and crews by addressing what we now call survivorship bias-the tendency to draw conclusions only from "survivors" while ignoring casualties that never made it back. This example illustrates how mathematical thinking operates. It wasn't complex equations that saved those planes but the ability to see beyond the obvious-to recognize what the data wasn't showing. This same principle applies whenever we evaluate success stories without considering those who failed along the way, whether in business, medicine, or education. The power of mathematics lies not in calculation but in its ability to extend common sense. While Wald's actual report contained equations, the fundamental insight required no advanced notation. Mathematics builds on intuitive concepts and extends them, serving as what Ellenberg calls an "atomic-powered prosthesis" for common sense-vastly multiplying its reach while remaining grounded in ordinary thinking.

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Kapitel 3

The Laffer Curve: Finding the Middle Path

When the Affordable Care Act was being debated, libertarians argued that America shouldn't move toward Swedish-style social policies because even Sweden was scaling back government. This seemingly reasonable argument reveals a fundamental mathematical error: assuming that more government always means less prosperity, and vice versa. Reality is better represented by a nonlinear curve with an optimal point somewhere in the middle-neither too much government nor too little produces the best outcomes. This nonlinear thinking has ancient roots in Aristotle and Horace, who recognized that virtue lies between extremes. Ironically, economic conservatives once championed this nonlinear thinking through the Laffer curve, famously sketched on a napkin during a 1974 dinner with Dick Cheney and Donald Rumsfeld. The curve illustrates that government revenue is zero when tax rates are either 0% (no taxes collected) or 100% (no incentive to work), with maximum revenue occurring somewhere in between. When top marginal rates were 70% in 1974, the idea that America was on the downslope of the curve-where tax cuts would increase revenue-gained traction among wealthy taxpayers. Wall Street Journal editor Jude Wanniski became the theory's zealous advocate, comparing himself to visionaries like Edison and Galileo. Reagan embraced the theory, but subsequent history failed to confirm Laffer's conjecture-when Reagan cut taxes, government revenue fell by 9% from 1980 to 1984, despite 4% economic growth. The fundamental insight of the Laffer curve-that the relationship between taxation and revenue is necessarily nonlinear-remains valid. The problem wasn't with the curve itself but with politicians who confused what could be true with what they wanted to be true. This illustrates a broader mathematical principle: the world rarely operates in straight lines, and understanding where you are on the curve determines which direction you should move.

Kapitel 4

Calculus in One Page: The World Through Newton's Eyes

Every smooth curve, when examined closely enough, resembles a straight line. This "straight locally, curved globally" principle applies to all smooth curves without sharp corners and represents the fundamental insight of calculus. Consider a missile's parabolic trajectory. Though gravity constantly curves its path earthward, zooming in on any tiny segment reveals what appears to be a straight line. The closer you look, the straighter it seems. Newton's conceptual leap was to go all the way-reducing your view to an infinitesimal point where the curve becomes exactly a line. The slope of this line is what we now call the derivative. Archimedes wouldn't have made this leap, refusing to say a circle actually was a polygon with infinitely many infinitely short sides. George Berkeley mockingly called Newton's infinitesimals "the ghosts of departed quantities." Yet calculus works perfectly in practice. A rock released from circular motion flies off in exactly the straight-line trajectory calculus predicts-demonstrating Newton's insight that objects naturally move in straight lines unless acted upon by other forces. This concept of the infinitely small has perplexed mathematicians for millennia, beginning with Zeno's paradoxes. His famous argument that to reach any destination, you must first travel halfway, then half the remaining distance, and so on infinitely-making all motion seemingly impossible. Though Diogenes famously "refuted" this by simply walking across a room, the mathematical puzzle persisted. The resolution came through Augustin-Louis Cauchy's notion of limits in the 1820s, which resolved these "unnecessary perplexities" that were often merely verbal disputes. Cauchy's approach sacrificed the uniqueness of decimal expansions (allowing both 1 and 0.999... to represent the same number) in favor of preserving arithmetic manipulations-a revolution that enraged his students and colleagues who wanted him to stick to traditional methods. But Cauchy stubbornly taught what he believed was true, driven by mathematics' unique joy of understanding something completely "the right way."

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Kapitel 5

The Obesity Apocalypse: When Straight Lines Lead to Absurdity

In 2008, a paper in the journal Obesity claimed all Americans would become overweight or obese by 2048. The media eagerly amplified this claim with headlines about an "obesity apocalypse," feeding America's latest moral panic. The problem with this prediction is fundamental: not every curve is a line. This misconception drives the misuse of linear regression, social science's most ubiquitous statistical tool. Linear regression finds the straight line that best approximates relationships between variables. While powerful when used appropriately, it becomes dangerously misleading when extrapolated beyond its range. The obesity researchers projected that 100% of Americans would be overweight by 2048 based on linear extrapolation of historical data. This demonstrates the absurdity of thoughtless linear projection-by 2060, they'd predict 109% of Americans would be overweight! In reality, as the proportion of overweight people increases, the rate of increase must slow down as fewer non-overweight people remain to convert. The curve naturally bends toward but never reaches 100%, and indeed, subsequent data showed the upward trend had already begun to slow. Even worse, the researchers' own analysis contained a mathematical contradiction: when breaking down projections by demographic groups, they found only 80% of Black men would be overweight by 2048-directly contradicting their headline claim that all Americans would be overweight. This fundamental inconsistency went unmentioned in the paper-the epidemiological equivalent of negative water in a bucket. This example illustrates a broader principle: when students get ridiculous answers (like negative water weight) but acknowledge "I screwed up somewhere," they earn partial credit, while those who blindly circle impossible answers get zero. This reflects a crucial distinction: computers can perform calculations, but humans must judge whether results make sense.

Kapitel 6

Dead Americans and Statistical Distortions

When discussing casualties in international conflicts, commentators frequently convert foreign death tolls to "American equivalents" using simple population proportions. For example, 1,074 Israeli deaths in the second intifada were described as "proportionally equivalent to more than 50,000 dead Americans." However, this "lineocentrism" breaks down under scrutiny. The same event yields wildly different "equivalents" depending on which populations you compare. Should 200 Madrid bombing victims equal 1,300 Americans (by national population), 463 (scaling Madrid to New York City), or 600 (comparing Madrid province to New York state)? This multiplicity of answers reveals the method's fundamental flaw. The problem stems from a statistical principle demonstrated through a coin-flipping analogy: when comparing groups flipping different numbers of coins, those flipping fewer coins will naturally show more extreme proportional results due to smaller sample sizes. This is formalized in the Law of Large Numbers, which explains why small samples are inherently more variable-whether in coin flips, NBA shooting percentages, or state cancer rates. Abraham de Moivre discovered that the typical discrepancy from expected probability grows with the square root of the sample size. Toss 100 times more coins, and the typical discrepancy grows by a factor of 10 in absolute terms, but shrinks proportionally. When measuring death tolls across nations of different sizes, the same statistical principle applies-smaller countries will show more extreme proportional losses. Rating massacres by percentage of population killed puts events like the Herero genocide in Namibia at the top, while Hitler, Stalin, and Mao's larger absolute death tolls don't make the list. Ellenberg suggests a rule of thumb: when a disaster's magnitude is so great we talk about "survivors," measuring deaths as a proportion of population makes sense-as with the Rwandan genocide that killed 75% of the Tutsi population. But for events like 9/11, where only 0.001% of Americans died, proportional measures don't work well.

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Kapitel 7

The Bible Code and the Baltimore Stockbroker

In the 1990s, researchers at Hebrew University began searching for hidden patterns in the Torah using "equidistant letter sequences" (ELSs), where letters are extracted at regular intervals to form words. Their paper, published in the respected journal Statistical Science, claimed to find the names of famous rabbis encoded alongside their birth and death dates at statistically significant rates. This seemingly miraculous finding illustrates how seemingly improbable events can be engineered to appear supernatural-similar to the Baltimore stockbroker con. In this scam, a broker sends different stock predictions to thousands of people, eliminating those who receive incorrect predictions after each round. After ten rounds, those who received only correct predictions believe the broker has extraordinary insight, when in reality, the apparent miracle was manufactured through large numbers. When mathematicians examined the Bible code claims, they discovered the researchers had considerable "wiggle room" in how they selected rabbinical names. Medieval rabbis were known by various appellations-for instance, Rabbi Avraham ben Dov Ber Friedman might be referenced as "Rabbi Avraham," "HaMalach" (the angel), or "Rabbi Avraham HaMalach." By making different choices about which appellations to use, skeptics showed they could make the Hebrew translation of War and Peace appear just as prescient as Genesis. This principle extends beyond hypothetical scams. Mutual fund companies routinely "incubate" numerous funds internally, then only publicly launch those with impressive performance histories while quietly eliminating underperformers. Despite their stellar pre-public records, these funds typically revert to average performance once available to investors. The lesson is that improbable events become probable given enough opportunities. As Aristotle noted, "it is probable that improbable things will happen." Whether it's matching lottery numbers, lightning strikes, or seemingly prescient investment advice-improbable things happen frequently because the universe provides countless opportunities for coincidences.

Kapitel 8

Dead Fish and Statistical Significance

Neuroscientist Craig Bennett's satirical study revealed how standard statistical methods could produce absurd results. His poster reported that a dead fish in an fMRI scanner appeared capable of accurately assessing human emotions in photographs. The experiment highlighted how easily false positives emerge when analyzing massive datasets. When scientists divide fMRI scans into tens of thousands of small regions (voxels), the sheer number of data points makes it statistically likely that random noise in some voxels will coincidentally align with experimental stimuli. Bennett found exactly this-two groups of voxels in the dead salmon's brain that appeared to "empathize" with human emotions. The more opportunities you have to be surprised, the higher your threshold for surprise should be. Bennett's most concerning finding was that many neuroimaging articles didn't use statistical safeguards ("multiple comparisons correction") that account for the ubiquity of improbable coincidences, making them vulnerable to the same error as the Baltimore stockbroker con. This problem extends beyond neuroscience to all fields using significance testing. The null hypothesis significance test, formalized by R.A. Fisher, became the backbone of scientific research. The procedure: run an experiment, calculate the p-value (the probability of getting results as extreme as observed if the null hypothesis is true), and if p is very small (traditionally below 0.05), declare the results "statistically significant." Despite its ubiquity, the method has faced criticism for nearly as long as it's been standard. A fundamental problem lies in the word "significance" itself-in statistics it merely means "not zero," but in everyday language it suggests "important" or "meaningful." This linguistic confusion has real consequences, as when a 1995 UK warning about "statistically significant" blood clot risks from certain contraceptive pills led to widespread panic, thousands of unplanned pregnancies, and increased abortions-all to potentially prevent just one death. The significance test is merely a scientific instrument with limited precision. The null hypothesis, taken literally, is almost always false-everything we do affects our bodies somehow. With sensitive enough tests, we can detect these effects, but that doesn't mean they matter.

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Kapitel 9

Bayesian Thinking in an Age of Big Data

The age of big data promises algorithms that can make superhuman inferences about us, which many find frightening. When Target correctly inferred a teenage girl's pregnancy before her father knew, it seemed eerily powerful. But perhaps we should worry less about eerily superpowered algorithms and more about crappy ones. Imagine Facebook developing an algorithm to identify potential terrorists among its users-similar to how Target identifies pregnant shoppers, but with much rarer targets. Even if Facebook created a list of users twice as likely as average to be terrorists, the mathematics of rare events creates a dangerous paradox. With 200 million US users and perhaps 10,000 potential terrorists (1 in 20,000), a list of 100,000 "high-risk" users would contain only about 10 actual terrorists. This means 99.99% of people on the list would be innocent. This reveals the crucial distinction between two questions that sound similar but aren't: 1. What's the chance someone gets flagged if they're not a terrorist? (Only about 0.05%) 2. What's the chance someone's not a terrorist if they're flagged? (99.99%) The p-value logic we use in science answers the first question, which might seem to justify rejecting the "null hypothesis" of innocence. But what we actually want is the answer to the second question-the conditional probability that reveals almost everyone flagged would be innocent. Bayesian inference helps us navigate these challenges by updating our beliefs based on evidence. When faced with evidence like five consecutive reds on a roulette wheel, Bayesian inference helps us update our beliefs about competing theories (fair wheel vs. biased wheel). Crucially, your posterior beliefs depend both on the evidence and your prior beliefs-a cynic and an optimist will reach different conclusions from the same data. This Bayesian framework explains why we find some patterns "less random" than others. RRRRR activates a theory (biased wheel) that we already assign some probability to, while RBRRB doesn't match any theory we consider plausible. Our priors aren't flat but spiky-we assign significant weight to simple theories while dismissing complex ones.

Kapitel 10

The Mathematics of Winning (and Losing) at Gambling

Should you play the lottery? While often called a "tax on the stupid," lotteries have a long history dating to 17th-century Genoa. Adam Smith criticized lotteries, claiming "the more tickets you adventure upon, the more likely you are to be a loser." But this isn't strictly correct. In a lottery with 10 million combinations, a $1 ticket price, and a $6 million jackpot, buying more tickets actually decreases your chance of losing money-up to a point. Expected value helps determine the right price for uncertain propositions. It isn't what you actually expect to receive, but rather the average outcome if you repeated the same bet many times. For a lottery ticket with a 1 in 10 million chance of winning $6 million, the expected value is 60 cents-not what you'll actually get, but the average return per ticket over time. Using expected value calculations, a $2 Powerball ticket with a $100 million jackpot returns only about 94 cents in expected value. However, when jackpots grow larger, the expected value improves. At a $337 million jackpot, the expected value rises to $2.29, seemingly making it worthwhile. But this calculation ignores crucial factors: as jackpots grow, more people play, increasing the likelihood of splitting the prize. Additionally, taxes and installment payments further reduce winnings. In July 2005, Massachusetts lottery officials received unusual reports of college students buying tens of thousands of Cash WinFall tickets at once. What seemed suspicious was actually brilliant mathematics. Cash WinFall had a unique feature: when jackpots exceeded $2 million without a winner, the money "rolled down" to enhance lesser prizes. On these roll-down days, the expected value of a ticket jumped to $5.53-far exceeding its $2 cost. Three sophisticated groups discovered this loophole and invested hundreds of thousands of dollars on roll-down days, virtually guaranteeing profits through the law of large numbers. The mathematical principle at work was additivity of expected value: while any single ticket likely lost money, buying in bulk made the average return predictably positive. The cartels' approach followed a simple maxim: if gambling is exciting, you're doing it wrong. By making enough bets with odds tilted in their favor, the sheer volume diluted any bad luck. Massachusetts ultimately collected $120 million in revenue from Cash WinFall-when you walk away with nine figures, you probably didn't get scammed.

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Kapitel 11

The Triumph of Mediocrity: Regression to the Mean

In the aftermath of the 1929 crash, Northwestern professor Horace Secrist published "The Triumph of Mediocrity in Business," a massive statistical analysis showing successful businesses inevitably regress toward mediocrity over time, as do underperforming ones. This statistical revelation descended from Francis Galton, the Victorian scientist who first identified regression to the mean. Galton discovered that tall parents have tall children, but those children are not likely to be as tall as their parents. The same applies in reverse for short parents. This "regression to the mean" occurs because height is determined by both hereditary factors and external forces like environment and chance. The tallest people typically have both good genes and favorable external factors. Their children inherit the genes but not necessarily the same lucky external circumstances. This explains Secrist's business findings too. Top-performing companies were both well-managed and lucky. Their management might remain superior, but their luck would naturally vary over time, causing regression toward average performance. Almost any condition involving random fluctuations exhibits this effect, from dieting success to artistic achievement. Second novels rarely match breakout debuts not because artists only have one thing to say, but because artistic success combines talent and fortune. Similarly, star athletes who sign big contracts after exceptional seasons typically perform worse afterward-not just from reduced motivation but because their exceptional performance partly resulted from temporary good fortune. Early baseball season always brings stories about players who are "on pace" for record-breaking feats. These projections represent false linearity-like claiming Matt Kemp's nine home runs in seventeen games would translate to 86 home runs over a full 162-game season. This linear projection fails because it ignores regression to the mean. League leaders in statistics like home runs are likely both skilled and lucky. While skill persists, luck doesn't, causing performance to regress toward their true ability level. Harold Hotelling, a mathematical prodigy who transitioned from journalism to become a leading statistician, delivered a devastating critique of Secrist's work. While acknowledging Secrist's enormous data collection effort, Hotelling pointed out that the "triumph of mediocrity" was mathematically inevitable whenever studying variables affected by both stable factors and chance. He delivered the killing blow with a simple observation: if Secrist's theory about competition causing regression were true, then looking backward in time from high-performing companies in 1922 would show them being more mediocre in 1916-meaning competitive forces would have to "work backward in time as well as forward."

Kapitel 12

How to Be Right: The Art of Uncertainty

Mathematics isn't just about certainty-it's a means to reason about uncertainty in a principled way. While Theodore Roosevelt celebrated those who strive and risk failure over critics who remain safely on the sidelines, Ellenberg counters with examples like Condorcet and Abraham Wald, who contributed greatly without "lifting a weapon in anger"-critics who counted because they were right. Against Roosevelt's hard-charging vision, Ellenberg offers John Ashbery's poem "Soonest Mended" as a more complex portrait of uncertainty and revelation. Ashbery's line "For this is action, this not being sure" becomes a personal mantra. While Roosevelt would have dismissed uncertainty as cowardice, Ellenberg argues that not being sure is the move of a strong person, "raised to the level of an esthetic ideal." Nate Silver represents this principled uncertainty in our time-a "Kurt Cobain of probability" who made quantitative forecasting massively popular. What made Silver effective was his willingness to treat uncertainty as a real, measurable thing rather than a weakness. While traditional political pundits insisted on definitive predictions about who would win elections, Silver gave probabilistic answers. Critics misunderstood this approach as "hedging," failing to recognize that Silver was actually tracking changes in probability accurately. Even mathematicians don't try to be beings of pure logic. A purely deductive thinker who believes contradictory facts would be logically obliged to believe every statement is both true and false-the "principle of explosion." This is how Captain Kirk disabled AIs, but humans don't reason this way. We can tolerate contradiction, which is essential for mathematical thinking like reductio ad absurdum proofs. Ellenberg shares the mathematical folk wisdom of trying to prove a theorem by day and disprove it by night. This approach hedges against wasting effort on false statements, but more importantly, failed attempts to disprove something often reveal why it must be true. This approach extends beyond mathematics-putting pressure on all your beliefs helps you understand why you believe what you believe. Mathematics teaches simple lessons without numbers: that the world has structure we can understand, that intuition works better with formal frameworks, and that mathematical certainty differs from everyday conviction. We use mathematics constantly-recognizing diminishing returns, understanding probability, making decisions based on possible futures, or finding that cognitive sweet spot where intuition runs on tracks laid by formal reasoning. We've been using mathematics since birth and likely never stop.

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Kernthemen in How not to be wrong: Die Kunst des mathematischen Denkens

mathematical reasoning
survivorship bias
nonlinear thinking
statistical literacy
applied common sense

Zitate aus How not to be wrong: Die Kunst des mathematischen Denkens

Mathematics is the extension of common sense.
Mathematics isn't about memorizing formulas-it's about developing X-ray vision.
The missing bullet holes told the real story.
The world rarely operates in straight lines.
Not every curve is a line.

Personen in How not to be wrong: Die Kunst des mathematischen Denkens

Jordan EllenbergAuthor and professor of mathematics
Abraham WaldMathematician who identified survivorship bias
Arthur LafferEconomist who theorized the tax revenue curve
Jude WanniskiJournal editor and advocate for the Laffer curve

Über den Autor

Jordan Ellenberg ist der New York Times-Bestsellerautor von How Not to Be Wrong: The Power of Mathematical Thinking und ein renommierter Mathematiker an der University of Wisconsin–Madison, wo er als John D. MacArthur Professor für Mathematik tätig ist.

Sein Buch untersucht, wie mathematische Prinzipien alltägliche Entscheidungen prägen, und verbindet präzise Analysen mit zugänglichem Storytelling, um Themen wie Statistik, Wahrscheinlichkeitsrechnung und Spieltheorie zu entmystifizieren.

Als Guggenheim-Stipendiat und zweifacher Goldmedaillengewinner der Internationalen Mathematik-Olympiade hat Ellenberg gefeierte Werke wie Shape: The Hidden Geometry of Information, Biology, Strategy, Democracy, and Everything Else verfasst, das die Rolle der Geometrie im modernen Leben beleuchtet.

Seine Essays in der New York Times, Slate und Wired sowie seine Auftritte bei NPR und in der Today Show festigen seinen Ruf als führender Vermittler komplexer Ideen. How Not to Be Wrong hat sich weltweit über eine Million Mal verkauft, wurde in 20 Sprachen übersetzt und gilt als ein Grundpfeiler der zeitgenössischen Populärwissenschaft.

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Jordan Ellenberg

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108

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Kapitel 11

How not to be wrong: Die Kunst des mathematischen Denkens

ÜberblickÜberblick über How not to be wrong: Die Kunst des mathematischen Denkens

Ein Buch. Viele Arten, es zu hören.

When Numbers Reveal the Hidden Structure of Reality

The Missing Bullet Holes: Seeing What Isn't There

Sieh jedes Wort, während du es hörst

The Laffer Curve: Finding the Middle Path

Calculus in One Page: The World Through Newton's Eyes

Hol mehr aus How not to be wrong: Die Kunst des mathematischen Denkens heraus

The Obesity Apocalypse: When Straight Lines Lead to Absurdity

Dead Americans and Statistical Distortions

Sieh jedes Wort, während du es hörst

The Bible Code and the Baltimore Stockbroker

Dead Fish and Statistical Significance

Hol mehr aus How not to be wrong: Die Kunst des mathematischen Denkens heraus

Bayesian Thinking in an Age of Big Data

The Mathematics of Winning (and Losing) at Gambling

Sieh jedes Wort, während du es hörst

The Triumph of Mediocrity: Regression to the Mean

How to Be Right: The Art of Uncertainty

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Kernthemen in How not to be wrong: Die Kunst des mathematischen Denkens

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Personen in How not to be wrong: Die Kunst des mathematischen Denkens

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FAQ zu diesem Buch

What is How Not to Be Wrong by Jordan Ellenberg about?

Who should read How Not to Be Wrong?

Is How Not to Be Wrong worth reading?

What is survivorship bias in How Not to Be Wrong?

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1,000,000+ curious minds.

1,000,000+ curious minds.

Ein Buch. Viele Arten, es zu hören.

When Numbers Reveal the Hidden Structure of Reality

The Missing Bullet Holes: Seeing What Isn't There

Sieh jedes Wort, während du es hörst

The Laffer Curve: Finding the Middle Path

Calculus in One Page: The World Through Newton's Eyes

Hol mehr aus How not to be wrong: Die Kunst des mathematischen Denkens heraus

The Obesity Apocalypse: When Straight Lines Lead to Absurdity

Dead Americans and Statistical Distortions

Sieh jedes Wort, während du es hörst

The Bible Code and the Baltimore Stockbroker

Dead Fish and Statistical Significance

Hol mehr aus How not to be wrong: Die Kunst des mathematischen Denkens heraus

Bayesian Thinking in an Age of Big Data

The Mathematics of Winning (and Losing) at Gambling

Sieh jedes Wort, während du es hörst

The Triumph of Mediocrity: Regression to the Mean

How to Be Right: The Art of Uncertainty

Du bist am Ende von How not to be wrong: Die Kunst des mathematischen Denkens angekommen

Nutzern gefiel auch

Mehr entdecken

Überblick über How not to be wrong: Die Kunst des mathematischen Denkens