
How Not to Be Wrong: The Power of Mathematical Thinking
4.4 stars from 5,054 reviews. One of the highest rated Nonfiction books we track.
Last checked: Jul 12, 2026
Good deal — this title is discounted today.
Price range: $1.99 – $14.99
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Fans of these authors will like this...
- Malcolm Gladwell
- Nassim Nicholas Taleb
- Steven Levitt
Fans of these books will like this...
- Freakonomics
- Thinking, Fast and Slow
- The Black Swan
Perfect for you if...
- You're curious about how the world actually works beneath the surface and tired of being bamboozled by bad statistics.
- You loved Freakonomics or Thinking, Fast and Slow but want something that teaches you to spot logical flaws yourself.
- You're allergic to maths textbooks but fascinated by real-world applications, from elections to lottery schemes to why tall parents have shorter kids.
Maybe not for you if...
- You want rigorous proofs and formal mathematical notation; this book translates maths into English, not the other way round.
- You're looking for a self-help book with easy answers; Ellenberg's point is that most easy answers are wrong.
- You get queasy when books jump between topics; this one hopscotches from baseball to Renaissance painting to Reaganomics.
About this book
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Price History
Kindle price over time. Green zones = periods when the book was free.
Currently on sale at $1.99 — 87% off peak. Price has dropped 12 times over the last 6 years. Goes on sale roughly every 190 days.
Sales Rank
Amazon sales rank: lower number = better selling book. #1 is the bestseller.
Currently around #131K in the Kindle store — a niche title.
Rating & Reviews
Purple line = star rating over time. Blue area = total review count growth.
Currently 4.4★ from 5,054 reviews
Gaining approximately 9 new reviews per month. Rating has remained stable at 4.4 over the tracking period.
Sale Prediction
Actual Predicted
This book is currently discounted at $1.99. It typically returns to around $14.99.
Prices shown in USD from Amazon US. We continuously pull new data to improve tracking. If dates look old, it usually means there has been no significant change since the last data point.
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