What will I learn about: Uncover how identifying everyday 'bullshit' protects your worldview and prevents manipulated information from improperly influencing your choices?

Uncover how identifying everyday 'bullshit' protects your worldview and prevents manipulated information from improperly influencing your choices.

What will I learn about: the deceptive art of 'paltering,' where individuals use technically true but out-of-context statements to purposefully mislead you while maintaining plausible deniability?

Understand the deceptive art of 'paltering,' where individuals use technically true but out-of-context statements to purposefully mislead you while maintaining plausible deniability.

What will I learn about: Brandolini's Principle, which explains why the effort required to debunk false information is significantly greater than the effort it takes to simply invent it?

Discover Brandolini's Principle, which explains why the effort required to debunk false information is significantly greater than the effort it takes to simply invent it.

What will I learn about: Learn how social media algorithms and the media's race to publish first fuel dangerous confirmation biases and the rapid, unchecked spread of fake news?

Learn how social media algorithms and the media's race to publish first fuel dangerous confirmation biases and the rapid, unchecked spread of fake news.

What will I learn about: Recognize why our deep-seated trust in numbers leaves us vulnerable to deception, and find out how easily statistics, data, and charts can be manipulated to skew reality?

Recognize why our deep-seated trust in numbers leaves us vulnerable to deception, and find out how easily statistics, data, and charts can be manipulated to skew reality.

Calling Bullshit

The Art of Skepticism in a Data-Driven World

Carl T. Bergstrom, Jevin D. West

★ 4.4 / 5(128 ratings)

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Categories:

Personal Development Science Society & Culture

If You're Curious About These Questions...

You should listen to this audiobook

💡Have you ever wondered why even the most "scientific" charts and data visualizations can be used to trick you without technically telling a single lie?
💡What if you could develop a personal "bullshit detector" sharp enough to see through the complex algorithms and fancy statistics often used to manipulate public opinion?
💡Did you know that it takes exponentially more energy to debunk misinformation than it does to create it—and are you curious about the secret to stopping it in its tracks?

Listen to Calling Bullshit — Free Audiobook

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Key Takeaways from Calling Bullshit

✓Uncover how identifying everyday 'bullshit' protects your worldview and prevents manipulated information from improperly influencing your choices.
✓Understand the deceptive art of 'paltering,' where individuals use technically true but out-of-context statements to purposefully mislead you while maintaining plausible deniability.
✓Discover Brandolini's Principle, which explains why the effort required to debunk false information is significantly greater than the effort it takes to simply invent it.
✓Learn how social media algorithms and the media's race to publish first fuel dangerous confirmation biases and the rapid, unchecked spread of fake news.
✓Recognize why our deep-seated trust in numbers leaves us vulnerable to deception, and find out how easily statistics, data, and charts can be manipulated to skew reality.

Learning Tools

Reinforce what you learned from Calling Bullshit

Mind Map

Calling Bullshit

Defining Bullshit+

Data & Algorithms+

Statistical Traps+

Selection Bias+

Scientific Imperfections+

Defense Mechanisms+

Quiz — Test Your Understanding

Question 1 of 7

According to the authors, what is the defining characteristic of a 'bullshitter'?

A. They intentionally spread malicious lies to destroy their opponents' reputations.
B. They care primarily about persuading or impressing others, regardless of whether their claims are supported by evidence.
C. They rely exclusively on complex algorithms and machine learning to make their arguments.
D. They fabricate data sets from scratch to ensure their scientific papers get published.

Question 2 of 7

Why did the 2016 study claiming an algorithm could identify criminals by their head shape produce 'bullshit' results?

A. The algorithm was intentionally programmed by the researchers to be racially biased.
B. The sample size of the study was too small to be statistically significant.
C. The data compared unsmiling government ID photos of criminals with smiling professional headshots of non-criminals.
D. The researchers falsified the mathematical outputs of the algorithm to ensure a positive result.

Question 3 of 7

How did the media misrepresent the Zillow report regarding rising house prices and fertility rates?

A. They falsely claimed that fertility rates were rising alongside house prices.
B. They assumed rising house prices directly caused lower fertility, rather than recognizing it as a mere correlation.
C. They completely ignored the data for women over the age of 30, which the original study highlighted.
D. They manipulated the percentages to make the drop in fertility look much larger than it actually was.

Question 4 of 7

Why is the common claim that 'switching car insurance saves an average of $500' a prime example of selection bias?

A. Car insurance companies use machine learning 'black boxes' to arbitrarily generate the $500 figure.
B. Insurance companies blatantly lie about the total amount of money they save their customers.
C. The $500 figure is calculated using Fermi estimations rather than real-world financial data.
D. Only people who will actually save a significant amount of money bother to switch, meaning the data sample is unrepresentative.

Question 5 of 7

What fundamental flaw caused the machine learning algorithm to fail when trying to identify unhealthy lungs from chest X-rays?

A. It learned to identify text printed on the scans by a specific machine, rather than actual medical issues.
B. It was trained on too few X-ray images to establish a reliable medical pattern.
C. It could not distinguish between human X-rays and animal X-rays fed into the system.
D. It suffered from Goodhart's Law, as doctors actively tried to game the algorithm to get faster diagnoses.

Question 6 of 7

How does Goodhart's Law apply to the problem of 'p-hacking' in modern scientific research?

A. It proves that any scientific study funded by a corporation will inevitably produce biased results.
B. It states that when achieving a p-value of 0.05 becomes the target for publication, scientists may manipulate their data to hit that metric.
C. It explains why only a small fraction of scientific research gets reported on by the mainstream media.
D. It demonstrates that peer-reviewed journals are inherently more reliable than independent studies.

Question 7 of 7

What is the primary purpose of using a 'Fermi estimation' when you encounter a suspicious claim?

A. To calculate the exact statistical significance of a scientific study.
B. To determine the hidden financial motivations of the person presenting the data.
C. To perform a rough mental calculation to see if a claimed number is plausible in scale.
D. To uncover whether an algorithm is acting as an inscrutable 'black box'.