In this podcast episode, we dive into the significance of the normal distribution, also known as the Gaussian distribution, in the realm of probability theory. It highlights the fascinating Central Limit Theorem, which states that when you sum multiple independent random variables, the result tends to follow a normal distribution. The episode visually illustrates how the convolution of two Gaussian functions yields another Gaussian, emphasizing the remarkable stability and self-similarity of this distribution. This unique quality is what establishes the normal distribution as the "universal shape" that emerges as we continuously add random variables together.
