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In this video, a senior data scientist tackles realworld interview questions, showcasing a strong understanding of probability and statistics. We learn how to apply Bayes' Theorem to solve conditional probability problems, gain a deeper understanding of pvalues and their role in hypothesis testing, and explore the relationship between confidence intervals and uncertainty.

Chapters (Powered by ChapterMe)
00:00 Conditional probabilities in factory production
01:50 Bayes theorem formula and conditional probabilities
06:07 Probability calculation for defective bulb
10:51 Pvalue in hypothesis testing
15:31 Pvalue for null hypothesis rejection
17:52 Pvalues inverse tdistribution explanation
18:17 Building confidence intervals in statistics
19:27 Confidence interval percentage of times expected to fall
20:25 Confidentially reproduces estimates between upper and lower bounds
25:08 Results Clearer, Simplifying, More Clear
26:06 Deep knowledge of p values, hypothesis testing, and concrete examples
27:34 Wrap up

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