May Contain Lies by Alex Edmans Summary

May Contain Lies examines how biases distort our interpretation of stories, statistics, and studies, leading to misinformation. Economist Alex Edmans uses real-world examples—like the Deepwater Horizon disaster and a fraudulent wellness guru—to illustrate how people confuse anecdotes for evidence. The book provides tools to critically analyze data relationships and causality, helping readers make informed decisions.

This book is essential for professionals, policymakers, and anyone navigating today’s information-saturated world. It’s particularly valuable for those seeking to improve critical thinking skills, discern factual accuracy in media, or avoid manipulation by biased narratives. Educators and students will also benefit from its frameworks for evaluating evidence.

Yes—Edmans combines rigorous analysis with engaging storytelling, making complex concepts like causal inference accessible. The book’s actionable advice, such as identifying “ladder of misinference” biases (e.g., mistaking data for proof), offers lifelong tools for smarter decision-making. Case studies, like a wrongful conviction due to confirmation bias, underscore its real-world relevance.

Edmans details four key biases: conflating statements with facts, facts with data, data with evidence, and evidence with proof. He demonstrates how these distortions fuel misinformation, using examples like the 10,000-hour rule’s misapplication and diets based on flawed studies.

The book advocates skepticism toward emotionally charged claims and teaches readers to scrutinize sources, sample sizes, and causal relationships. For instance, Edmans critiques the “side of a bus” fallacy—assuming bold claims (like political slogans) are factual without verification.

Notable examples include BP’s Deepwater Horizon oil spill (highlighting leadership overconfidence), a wrongful 20-year imprisonment due to confirmation bias, and the rise and fall of the Cambridge Diet. These illustrate how misinformation spreads when biases override evidence.

Edmans emphasizes analyzing relationships between data points, not just individual statistics. He introduces frameworks to distinguish correlation from causation and assess study limitations, helping readers avoid pitfalls like survivorship bias or cherry-picked data.

This concept describes the progressive errors in reasoning: treating opinions as facts, isolated facts as comprehensive data, data as validated evidence, and evidence as irrefutable proof. Edmans shows how each step amplifies misinformation risks.

Yes—Edmans dissects how Malcolm Gladwell’s popularized “10,000-hour rule” (from Outliers) oversimplifies original research. The book argues that deliberate practice alone doesn’t guarantee expertise, highlighting flawed interpretations of Anders Ericsson’s studies.

The book warns against relying on charismatic leadership narratives or cherry-picked KPIs. For example, it critiques companies that prioritize short-term metrics over long-term sustainability, using Enron-style failures as cautionary tales.

Key lines include:

• “Doubt the side of a bus”—question bold claims presented as facts.
• “A fact is a datapoint, not destiny”—challenging deterministic interpretations of data.

These underscore the book’s theme of skeptical, evidence-based inquiry.

Amid AI-driven misinformation and polarized media, Edmans’ tools help navigate complex issues like climate debates or healthcare trends. The book’s focus on causal reasoning is crucial for evaluating emerging technologies and policy claims.