theory

I was reading a paper on calculation of sample sizes, and I inevitably came across the topic of statistical power. Essentially, when you’re designing on experiment, the sample size is an important factor to consider due to limiting resources. You want to have a sample size that is neither too small (which could result in high chance of failure to detect true differences) nor too big (potential waste of resources, albeit yielding better estimation). [Read More]

In my first machine learning class, in order to learn about the theory behind PCA (Principal Component Analysis), we had to learn about variance-covariance matrix. I was concurrently taking a basic theoretical probability and statistics, so even the idea of variance was still vague to me. Despite the repeated attempts to understand covariance, I still had trouble fully capturing the intuition behind the covariance between two random variables. Even now, application and verification of correct usage of mathematical properties of covariance requires intensive googling. [Read More]