Calling Bullshit

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".

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.

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.

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.

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.

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.

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.

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.

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).

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.

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.

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.