Calling Bullshit: The Art of Skepticism in a Data-Driven World by Carl T. Bergstrom Summary

1. New-school bullshit uses math and science language to disguise falsehoods
2. Brandolini’s principle: Debunking bullshit takes 10x more effort than creating it
3. Detect bullshit by questioning data sources and statistical interpretations
4. Goodhart’s law reveals targeted metrics become unreliable over time
5. Use reductio ad absurdum to expose flawed arguments through absurd consequences
6. Mathiness mimics rigor with formulas that lack logical coherence
7. Counterexamples dismantle generalizations by showcasing real-world exceptions
8. Clarifying analogies parallel bullshit arguments to highlight their invalidity
9. Black box analysis focuses on input quality and output plausibility
10. Base rate fallacy ignores prior probabilities in statistical claims
11. Technology amplifies bullshit spread via bots and viral misinformation
12. Critical thinking combats bullshit by prioritizing facts over identity biases

Über den Autor von Calling Bullshit

Carl T. Bergstrom, co-author of Calling Bullshit: The Art of Skepticism in a Data-Driven World, is a renowned theoretical biologist and misinformation analyst. A professor at the University of Washington, Bergstrom merges evolutionary biology with social network dynamics, focusing on how information—and disinformation—spreads in modern society. His expertise in data literacy and scientific communication stems from decades of research, including co-developing the Eigenfactor metric for academic journal evaluation, which earned him and his father the SPARC Innovator Award.

Bergstrom’s work extends beyond academia. His Calling Bullshit course materials, developed with Jevin West, are used globally to teach critical thinking in the digital age. During the COVID-19 pandemic, he became a leading voice in combating health misinformation. He also co-authored the widely used textbook Evolution (with Lee Dugatkin), bridging biological principles with contemporary scientific discourse.

Calling Bullshit distills Bergstrom’s research into actionable tools for deciphering data claims, reflecting his mission to empower public skepticism. The book emerged from his viral University of Washington course, now a cornerstone of information literacy education.

FAQ zu Calling Bullshit

What is Calling Bullshit by Carl T. Bergstrom about?

Calling Bullshit provides tools to identify and combat misleading claims in data, media, and science. Co-authored by biologist Carl Bergstrom and data scientist Jevin West, it teaches critical thinking to navigate misinformation using real-world examples like skewed charts, biased studies, and deceptive correlations. The book emphasizes logical reasoning over technical expertise to debunk modern "bullshit".

Who should read Calling Bullshit?

Professionals, students, and anyone exposed to data-driven arguments (e.g., journalists, policymakers) will benefit. It’s particularly valuable for social media users, researchers, and educators seeking to recognize flawed statistics, cherry-picked data, or misleading visualizations. The authors avoid jargon, making it accessible to non-experts.

Is Calling Bullshit worth reading?

Yes—it’s a timely guide for navigating misinformation in the AI and social media era. The blend of academic rigor (Bergstrom’s expertise in evolutionary biology; West’s data science background) and practical examples makes complex concepts like selection bias or data misrepresentation easy to grasp. Readers gain actionable strategies to combat misinformation personally and professionally.

How does Calling Bullshit define "bullshit"?

The authors define bullshit as claims that persuade through emotional appeal or authority rather than evidence. Unlike lies (intentional falsehoods), bullshit often stems from negligence, such as misinterpreting correlations as causation or using skewed graphs. Examples include viral health myths and politicized data.

What frameworks does Calling Bullshit teach for detecting misinformation?

Key strategies include:

• Source scrutiny: Assessing conflicts of interest or funding biases.
• Data triangulation: Cross-verifying claims with independent sources.
• Logical consistency: Flagging mismatches between data and conclusions.
• Visual literacy: Identifying misleading axes or cherry-picked timeframes in charts.

How does Calling Bullshit address flaws in scientific research?

The book critiques "p-hacking" (manipulating data to achieve statistical significance), publication bias, and predatory journals. Bergstrom’s work on the Eigenfactor journal-ranking system informs discussions about incentivizing rigorous research over sensationalism.

What are real-world examples of bullshit debunked in the book?

Case studies include:

• Misleading COVID-19 mortality rate comparisons.
• Corporate greenwashing via selective sustainability metrics.
• Political misuse of crime statistics to fearmonger.

Each example demonstrates how basic logic and fact-checking expose flaws.

How does Calling Bullshit critique big data and AI?

It warns that large datasets can amplify biases (e.g., racist facial recognition algorithms) and create false patterns. The authors stress that "big data" requires big scrutiny—highlighting how tech firms often prioritize correlation over causation.

What criticisms exist about Calling Bullshit?

Some reviewers note it focuses more on identifying bullshit than systemic solutions. Others argue its academic tone may limit appeal to general audiences, though the authors counter with humor and pop culture references (e.g., dissecting viral memes).

How does Calling Bullshit compare to Factfulness or Weapons of Math Destruction?

Unlike Factfulness (which focuses on global progress) or Weapons (which critiques algorithms), Calling Bullshit offers a toolkit for everyday skepticism. It overlaps in discussing data ethics but stands out with its academic roots and UW course-tested methods.

Can Calling Bullshit help improve media literacy?

Yes—it teaches readers to dissect news headlines, social media posts, and scientific claims by asking:

1. Who benefits? Follow the money or agenda.
2. Is the sample size/data source valid?
3. Does the visualization distort proportions?

These skills combat echo chambers and confirmation bias.

Why is Calling Bullshit relevant in 2025?

With AI-generated deepfakes and algorithmic misinformation rising, the book’s lessons on source verification and logical fallacies remain critical. Bergstrom’s ongoing work on disinformation dynamics (e.g., tracking viral conspiracy theories) reinforces its urgency.