ML Data Science Coding Question - Calculate the Probability of a Fair Coin and Simulate It

Ace your next interview with Exponent's courses at the links below!

In this video, we tackle a sample coding interview question and explore two approaches to solve it: a theoretical one using SciPy and a hands-on simulation with NumPy.

Chapters:
00:00 - Question 1: Handling Exponents in Jupyter Notebooks
03:19 - Understanding Binomial Distributions with ScipyStats
08:43 - Analyzing Cumulative Distribution Functions (CDF) for Probability
10:20 - Core Interview Concepts: P-Values, Probability Mass Functions (PMFs), and CDFs
10:37 - Question 2: Probability Simulations for Real-World Applications
17:04 - Using List Comprehension and Simulation to Solve Probability Questions
18:05 - Insights on P-Values and Statistical Significance in Decision Making
20:37 - Evaluating Coin Fairness for Business Decisions
21:56 - Conclusion

Want more machine learning content?

ABOUT US:
Did you enjoy this interview question and answer? Want to land your dream career? Exponent is an online community, course, and coaching platform to help you ace your upcoming interview. Exponent has helped people land their dream careers at companies like Google, Microsoft, Amazon, and high-growth startups. Exponent is currently licensed by Stanford, Yale, UW, and others.

Our courses include interview lessons, questions, and complete answers with video walkthroughs. Access hours of real interview videos, where we analyze what went right or wrong, and our 1000+ community of expert coaches and industry professionals, to help you get your dream job and more!

[tryexponent]

Ace your next interview with Exponent's courses at the links below!

[Kuan-TingChen]

Thank you for sharing this video.
However, I noticed that you only used a one-tailed test to examine the coin fairness. According to theory, when we want to test if a coin is fair, we typically employ a two-tailed test because we are interested in whether the coin deviates from fairness, whether towards heads or tails. And the way you use if more like to examine H1 = the coin is not fair and "in favor of head" and ignore the case when coin is in favor of tail.
In this way, using a two-tailed test can comprehensively detect potential biases. Please clarify me if there is any detail I missed, thank you!