Here are 69 books that Calling Bullshit fans have personally recommended if you like
Calling Bullshit.
Shepherd is a community of 12,000+ authors and super readers sharing their favorite books with the world.
I’m an emeritus professor of psychology (University of Washington) who has long been intrigued by the mistakes that people have made throughout history. I’ve long been struck by Oppenheimer’s observation, immediately after the Trinity explosion, that “I am become Death, the destroyer of worlds.” This led me to look into the wide array of mistakes, from the mythic, literary, athletic, business, political, medical, and military. In writing OOPS!, I let myself go in a way that I’ve never before, writing with a critical and wise-ass style that isn’t strictly academic, but is factually accurate and, frankly, was a lot of fun!
Two renowned social psychologists show how people—some famous and some not—avoid taking responsibility for their blunders.
By the book''s end, we see how we avoid admitting our missteps, and aware of how much our own (and everyone's) lives would improve if we could simply say, ''I made a mistake. I'm sorry.”
Renowned social psychologists Carol Tavris and Elliot Aronson take a compelling look into how the brain is wired for self-justification. This updated edition concludes with an extended discussion of how we can live with dissonance, learn from it, and perhaps, eventually, forgive ourselves.
Why is it so hard to say “I made a mistake”—and really believe it?
When we make mistakes, cling to outdated attitudes, or mistreat other people, we must calm the cognitive dissonance that jars our feelings of self-worth. And so, unconsciously, we create fictions that absolve us of responsibility, restoring our belief that we are smart, moral,…
As a wannabe rockstar studying philosophy and mathematics, never in my wildest nightmare did I imagine I would one day earn a living traveling the world, helping corporate managers become better bosses. But in unexpected ways, all the different strands of my interests and passions have woven together into a work-life well lived, with over two decades of experience and contemplation distilled down into this book I have co-written with my friend and business partner, Bjorn Billhardt, CEO of Abilitie.
There are so many golden calves in the world of management and leadership theory, and this book knocks nine of them down politely but mercilessly.
My favorite chapter: “The Delusion of Rigorous Research,” coming from a business school professor who knows first-hand what he’s talking about. I’m encumbered by philosophical training, and in the business world, I constantly find myself asking, “Yes, but what does that word actually mean?” or “What kind of evidence could support that claim, and is that evidence you could actually collect?”
More and more content about how to succeed in business and management gets produced by humans, and increasingly by AI. In that context, I’m grateful for books like this one that focus more on “how” than on “what” to think.
Why do some companies prosper while others fail? Despite great amounts of research, many of the studies that claim to pin down the secret of success are based in pseudoscience. The Halo Effect is the outcome of that pseudoscience, a myth that Philip Rosenzweig masterfully debunks in THE HALO EFFECT. The Halo Effect describes the tendency of experts to point to the high financial performance of a successful company and then spread its golden glow to all of the company's attributes - clear strategy, strong values, and brilliant leadership. But in fact, as Rosenzweig clearly illustrates, the experts are not…
I am the Fletcher Jones Professor of Economics at Pomona College. I started out as a macroeconomist but, early on, discovered stats and stocks—which have long been fertile fields for data torturing and data mining. My book, Standard Deviations: Flawed Assumptions, Tortured Data, and Other Ways to Lie with Statistics is a compilation of a variety of dubious and misleading statistical practices. More recently, I have written several books on AI, which has a long history of overpromising and underdelivering because it is essentially data mining on steroids. No matter how loudly statisticians shout correlation is not causation, some will not hear.
A biting quip in the debate about whether computers are on the verge of surpassing (or have already surpassed) human intelligence is, “It is not that computers are getting smarter but that humans are getting dumber.”
In the same spirit, Nichols argues that “These are dangerous times. Never have so many people had access to so much knowledge, and yet been so resistant to learning anything.”
People are now exposed to more information than ever before, provided both by technology and by increasing access to every level education. These societal gains, however, have also helped fuel a surge in narcissistic and misguided intellectual egalitarianism that has crippled informed debates on any number of issues. Today, everyone knows everything; with only a quick trip through WebMD or Wikipedia, average citizens believe themselves to be on an equal intellectual footing with doctors and diplomats. All voices, even the most ridiculous, demand to be taken with equal seriousness, and any claim to the contrary is dismissed as undemocratic elitism.…
I’ve been interested in philosophy ever since I heard the album Poitier Meets Plato, a product of the 60’s coffee house culture, in which Sidney Poitier reads Plato to jazz music. As a professional philosopher, I investigate the nature of knowledge and reality, and if paranormal claims turn out to be true, many of our beliefs about knowledge and reality may turn out to be false. In an attempt to distinguish the justified from the unjustified—the believable from the unbelievable—I’ve tried to identify the principles of good thinking and sound reasoning that can be used to help us make those distinctions.
I learned from Gilovich the psychological mechanisms that drive us to believe things that aren’t true. We are pattern-recognizing machines, he tells us, designed to make sense of the data we perceive. But when that data is incomplete, ambiguous, or inconsistent, the mechanisms that normally yield correct inferences can lead us astray.
Thomas Gilovich offers a wise and readable guide to the fallacy of the obvious in everyday life.
When can we trust what we believe-that "teams and players have winning streaks," that "flattery works," or that "the more people who agree, the more likely they are to be right"-and when are such beliefs suspect? Thomas Gilovich offers a guide to the fallacy of the obvious in everyday life. Illustrating his points with examples, and supporting them with the latest research findings, he documents the cognitive, social, and motivational processes that distort our thoughts, beliefs, judgments and decisions. In a rapidly changing…
I am the Fletcher Jones Professor of Economics at Pomona College. I started out as a macroeconomist but, early on, discovered stats and stocks—which have long been fertile fields for data torturing and data mining. My book, Standard Deviations: Flawed Assumptions, Tortured Data, and Other Ways to Lie with Statistics is a compilation of a variety of dubious and misleading statistical practices. More recently, I have written several books on AI, which has a long history of overpromising and underdelivering because it is essentially data mining on steroids. No matter how loudly statisticians shout correlation is not causation, some will not hear.
Ritchie was part of a team that attempted to replicate a famous study led by a prominent psychologist, Daryl Bem, claiming that people did better on a word memorization test if they studied the words after taking the test.
Ritchie and his co-authors attempted to replicate this study and found no evidence supporting Bem’s claim. This is but one example of a scientific crisis in that attempts to replicate influential studies published in top peer-reviewed journals fail nearly half the time. Ritchie explains and illustrates the reasons for the current replication crisis in science.
An insider’s view of science reveals why many scientific results cannot be relied upon – and how the system can be reformed.
Science is how we understand the world. Yet failures in peer review and mistakes in statistics have rendered a shocking number of scientific studies useless – or, worse, badly misleading. Such errors have distorted our knowledge in fields as wide-ranging as medicine, physics, nutrition, education, genetics, economics, and the search for extraterrestrial life. As Science Fictions makes clear, the current system of research funding and publication not only fails to safeguard us from blunders but actively encourages bad…
I had an epiphany at Amazon, when we ran A/B tests at scale and observed the low success rate: we learned to adjust our intuitions. I recall the denial at Microsoft when I proposed to evaluate features with A/B tests “because over 50% of them failed to improve key metrics at Amazon.” The typical response? We have better program managers. When we started to evaluate ideas at Microsoft, over 2/3 of them failed to improve key metrics, and at Bing, the rate was about 80%. By 2019, most large products at Microsoft were making data-driven decisions with over 100 A/B test treatments launched every workday. I currently teach an A/B Testing class.
This great management book was written by an engineer who clearly explains the rationale for his recommendations.
Andy Grove, former chairman and CEO of Intel, is credited with driving the growth phase of Silicon Valley, was named Time’s Man of the Year, and is credited as the “Father of OKRs.”
One example that I love: a new hire does poor work. His manager says: “He has to make his own mistakes…that’s how he learns!”
Grove writes: “absolutely wrong…the tuition is paid by his customers…[instead of by the manager].” Another gem: “Review rough drafts [you delegated]; don't wait until your subordinates have spent time polishing them into final form before you find out that you have a basic problem with the contents.”
The president of Silicon Valley's Intel Corporation sets forth the three basic ideas of his management philosophy and details numerous specific techniques to increase productivity in the manager's work and that of his colleagues and subordinates
I had an epiphany at Amazon, when we ran A/B tests at scale and observed the low success rate: we learned to adjust our intuitions. I recall the denial at Microsoft when I proposed to evaluate features with A/B tests “because over 50% of them failed to improve key metrics at Amazon.” The typical response? We have better program managers. When we started to evaluate ideas at Microsoft, over 2/3 of them failed to improve key metrics, and at Bing, the rate was about 80%. By 2019, most large products at Microsoft were making data-driven decisions with over 100 A/B test treatments launched every workday. I currently teach an A/B Testing class.
This insightful book on evidence-based management is rich with great references. It is filled with provoking examples of things we heard or learned, which were debunked, making you think twice about them (Twyman’s law).
Do financial incentives work? They can, but in limited scenarios where individuals could be measured and minimum quality levels kept high; they may, however, backfire and undermine teamwork and attract the wrong people. When hiring, can the best talent be identified? Even in sports, some of the best players aren’t identified early in their careers.
Rembrandt is considered a great painter; Mozart, one of the greatest composers, but they were largely unrecognized and unrewarded during their lifetimes. Does supervisor feedback help? In some scenarios, only the supervisors think so.
The best organizations have the best talent...Financial incentives drive company performance...Firms must change or die. Popular axioms like these drive business decisions every day. Yet too much common management "wisdom" isn't wise at all--but, instead, flawed knowledge based on "best practices" that are actually poor, incomplete, or outright obsolete. Worse, legions of managers use this dubious knowledge to make decisions that are hazardous to organizational health. Jeffrey Pfeffer and Robert I. Sutton show how companies can bolster performance and trump the competition through evidence-based management, an approach to decision-making and action that is driven by hard facts rather than half-truths…
My research and writing in the field of emergency or disaster management has been focused on the concept of hazard mitigation. This means reducing the impact of disasters, the creation of hazard resilient and sustainable communities, and the application of scientific and technical expertise to the task. We all live in a world where it has become more important than ever to make intelligent decisions driven by a comprehension of the properties of the physical universe. It is also a world in which economic self-interest and political interests may impede that idealistic goal. I have a sense of urgency about reducing the efficacy of such impediments.
We are living in a time when ideology, myth, superstition, and ignorance seem to have more influence than ever in shaping our future.
This excellent and important book by Shawn Otto explains the forces (political and economic) at work that are aimed at undermining the role that knowledge and rational empirical evidence might play in making rational public policy decisions. This includes the effort to weaken the role that science might ideally play in helping us to address real-world challenges.
As one who studies and seeks to assess disaster preparedness, response, and mitigation, I have come to believe this “war” on science holds the potential to give birth to ever greater and more expensive (especially in terms of human costs) disasters.
Winner of the MN Book Award for Nonfiction. "Wherever the people are well informed," Thomas Jefferson wrote, "they can be trusted with their own government." But what happens when they are not? In every issue of modern society--from climate change to vaccinations, transportation to technology, health care to defense--we are in the midst of an unprecedented expansion of scientific progress and a simultaneous expansion of danger. At the very time we need them most, scientists and the idea of objective knowledge are being bombarded by a vast, well-funded, three-part war on science: the identity politics war on science, the ideological…
I am a leader in analytics and AI strategy, and have a broad range of experience in aviation, energy, financial services, and the public sector. I have worked with several major organizations to help them establish a leadership position in data science and to unlock real business value using advanced analytics.
This is a foundational book on analytics and data science as a business function and helped to shape the development of the practice. It provides a view of the discipline through a business lens and avoids deep technical examinations. Though much has changed in the 15 years since it was originally published, it is still essential reading for a leader in the field. No book since has captured as well the competitive differentiation that analytics provides.
You have more information at hand about your business environment than ever before. But are you using it to "out-think" your rivals? If not, you may be missing out on a potent competitive tool. In Competing on Analytics: The New Science of Winning, Thomas H. Davenport and Jeanne G. Harris argue that the frontier for using data to make decisions has shifted dramatically. Certain high-performing enterprises are now building their competitive strategies around data-driven insights that in turn generate impressive business results. Their secret weapon? Analytics: sophisticated quantitative and statistical analysis and predictive modeling. Exemplars of analytics are using new…
In sixth grade, my teacher tried to teach the class how to read line charts – and something fell into place for me. Ever since then, I’ve tried to sort data into forms that we can use to make sense of it. As a researcher at Microsoft, I consulted with teams across the organization – from sales to legal; and from Excel to XBox – to help them understand their data. At Honeycomb, I design tools for software operations teams to diagnose their complex systems. These books each gave me an “ah-hah” moment that made me think differently about the craft of creating visualization. They now sit on my shelf in easy reach – I hope you find them fascinating too.
I learned Tukey’s name about as soon as I learned that data visualization existed as more than a menu in Excel and a personal obsession. Tukey coined the term “exploratory data analysis,” and so tapped into a passion for swimming around in all the interesting rows and columns. Tukey was working before computers were widespread, and so I got a view of how he saw data: working against the constraints of pencil and paper; keeping your hand moving as fast as possible. While the explorations we can do with gigabytes of memory and powerful rendering are very different, the goal of getting information into your head as fast as possible is unchanged.
This title is part of the Pearson Modern Classics series. Pearson Modern Classics are acclaimed titles at a value price. Please visit www.pearson.com/statistics-classics-series for a complete list of titles.
The approach in this introductory book is that of informal study of the data. Methods range from plotting picture-drawing techniques to rather elaborate numerical summaries. Several of the methods are the original creations of the author, and all can be carried out either with pencil or aided by hand-held calculator.
0134995457 / 9780134995458 EXPLORATORY DATA ANALYSIS (CLASSIC VERSION), 1/e
