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By the end of this article, you will have a concrete understanding of Bayesian Statistics and its associated concepts. Therefore, it is important to understand the difference between the two and how does there exists a thin line of demarcation!It is the most widely used inferential technique in the statistical world.With this idea, I’ve created this beginner’s guide on Bayesian Statistics.I’ve tried to explain the concepts in a simplistic manner with examples.Similarly, intention to stop may change from fixed number of flips to total duration of flipping.
At each of 20 iterations I generate one new data point from the Binomial(10, 0.5) distribution and update the posterior with it.For example: Person A may choose to stop tossing a coin when the total count reaches 100 while B stops at 1000.For different sample sizes, we get different t-scores and different p-values.Being amazed by the incredible power of machine learning, a lot of us have become unfaithful to statistics.Our focus has narrowed down to exploring machine learning. We fail to understand that machine learning is not the only way to solve real world problems.