Probability and Statistics in Coin Tossing Experiments

Authors

  • Yihan Chen Nanjing University of Finance and Economics, Nanjing, China

DOI:

https://doi.org/10.54097/ymy0jt23

Keywords:

Probability, Statistics, Bernoulli Trial, Law of Large Numbers.

Abstract

This paper explores the fundamental principles of probability and statistics through the classic coin tossing experiment. By framing each toss as a Bernoulli Trial, we model sequences of flips using the Binomial Distribution to derive theoretical probabilities and Expected Value. The study then contrasts these predictions with Empirical Probability gathered from actual experiments, demonstrating the powerful convergence described by the Law of Large Numbers. Finally, the application of Hypothesis Testing to sample data is discussed, showcasing the statistical methodology used to validate assumptions about a coin's fairness against observed outcomes.

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References

[1] Kruskal, J. "Testing the Fairness of a Coin" (Informal Lecture Notes).

[2] "Computer Simulation of Coin Tossing" - Monte Carlo Method Application.

[3] Keller, J. B. (1986). "The Probability of Heads". *American Mathematical Monthly*.

[4] Diaconis, P., Holmes, S., & Montgomery, R. (2007). "Dynamical Bias in the Coin Toss". *Statistical Science*.

[5] Miller, J. B., & Sanjurjo, A. (2018). "The Three-Sigma Rule of Coin Tossing". *Journal of Behavioral and Experimental Economics*.

[6] Lindley, D. (1985). "A Bayesian Test for Fairness of Coins". *Journal of the American Statistical Association*.

[7] Fay, M. P., & Sterne, J. A. C. (2010). "How Many Coin Tosses Are Needed to Detect Bias?". *Statistics in Medicine*.

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Published

23-12-2025

How to Cite

Chen, Y. (2025). Probability and Statistics in Coin Tossing Experiments. Highlights in Science, Engineering and Technology, 159, 440-447. https://doi.org/10.54097/ymy0jt23