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Probability Quote by Siddhartha Mukherjee

“Perhaps the most striking illustration of Bayes’s theorem comes from a riddle that a mathematics teacher that I knew would pose to his students on the first day of their class. Suppose, he would ask, you go to a roadside fair and meet a man tossing coins. The first toss lands “heads.” So does the…” quote by Siddhartha Mukherjee
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““Perhaps the most striking illustration of Bayes’s theorem comes from a riddle that a mathematics teacher that I knew would pose to his students on the first day of their class. Suppose, he would ask, you go to a roadside fair and meet a man tossing coins. The first toss lands “heads.” So does the second. And the third, fourth . . . and so forth, for twelve straight tosses. What are the chances that the next toss will land “heads” ? Most of the students in the class, trained in standard statistics and probability, would nod knowingly and say: 50 percent. But even a child knows the real answer: it’s the coin that is rigged. Pure statistical reasoning cannot tell you the answer to the question—but common sense does. The fact that the coin has landed “heads” twelve times tells you more about its future chances of landing “heads” than any abstract formula. If you fail to use prior information, you will inevitably make foolish judgments about the future. This is the way we intuit the world, Bayes argued. There is no absolute knowledge; there is only conditional knowledge. History repeats itself—and so do statistical patterns. The past is the best guide to the future.””

Siddhartha Mukherjee

About This Quote

Source Speech: Lecture on Bayesian Reasoning, 2020

Relying solely on abstract probability ignores real‑world clues; observing a biased coin suggests it is rigged, so prior evidence shapes future expectations.

In simple terms: Ignoring prior evidence leads to wrong predictions.

Key Takeaway

Consider context before applying formulas.

Themes

statistics probability critical thinking decision making

Mood

thoughtful analytical

Type

educational inspirational

When to use this quote

  • classroom teaching
  • risk assessment
  • medical diagnosis
  • financial forecasting

Key Concepts

Bayesian inference conditional knowledge heuristics

Questions to Reflect On

  • How do you incorporate real‑world cues into probabilistic judgments?
  • When might a simple heuristic outperform formal analysis?
A Different Perspective

Pure statistical models may miss hidden biases.

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