I always knew Principal Component Analysis (PCA) as a dimensionality reduction technique. Way too many features? PCA. Need to visualize clustering? PCA. Exploratory data analysis? PCA. It’s definitely one of my go-to analysis back then, but not because of its usefulness—it was one of the few tools I knew existed, so might as well, I thought. When I ventured deeper into statistics, this was one of the first few things I mastered because it was a technique I used so often. Like I mentioned, PCA is a powerful tool for dimensionality reduction, feature engineering, and data visualization. Essentially, it linearly…

During my early days of learning statistics, I encountered a pretty interesting phenomenon while reading a (relatively) ancient article. The story goes like this: In the fall of 1973, a study on gender bias among graduate school admissions to University of California, Berkeley made headlines. The reason for this was that the admission figures showed that out of almost 13,000 applicants, the men had a 44% chance of being admitted while the women only had a 35% chance. This stark difference sent a clear message to the masses—Berkeley was simply discriminating women and prioritized education for men. Or was it?…

Back in university, my genetics professor introduced the concept of chi-square tests like it was a magical instrument. Before I was ever interested in any statistics, I always had a script from the lecture notes that ran some code on R, and all I had to do was reject either the hypothesis that my two variables were related or unrelated. The me back then wouldn’t ever fathom a version of myself so deeply invested in learning the foundations of many statistical models. Like, did you know that there are two types of chi-square tests? They are the: Although the two…