p-value Explained from first principles

The p-value, i.e., statistical significance is a cornerstone of evidence-based science including physics, chemistry, psychology, and sociology. It is equally important in understanding data analysis in corporations. Although, I would wager that most people don’t care, I would like to emphasize that one must understand what statistical significance is key to understanding whether your results are just a fluke. However, I personally feel like that the way most statisticians talk about it, it creates a lot of mystique and misunderstandings and only a bit of clarification. I have seen quite a few data-analysts who do not have a clear understanding of it even though they are able to produce the value through their software tools. However, a lack of understanding creates a space for misinterpretations, so I thought I would try to clarify it.

Fourier Transform - Part 1

There are plenty of literature explaining Fourier Transform. However, at least most of it is fairly terse and, as I feel, are difficult to understand. I will try to give a very simple and easy to follow introduction. I will explain the idea in three different ways, giving three different ways to look at the concept. The first way is through the lens of polynomial interpolation. The second is the way of actually visualizing how the waves interfere to produce the function, and the third is seeing functions as vectors. Let us start with polynomial interpolation This would be a series of blogs, the first starting this one. We will cover the interpolation view for the discrete Fourier transform in this article.