# Articles by tag: statistics

Science and statistics are hard. There are lots of reasons that can make things go wrong, and it's important to remember that when looking at p-values and hypothesis tests.

Differential analysis using sequencing data is, at its heart, a very simple idea that involves a lot of complicated statistics. It makes explaining the simple idea to newcomers in bioinformatics very difficult. Here, I want to break down the motivation behind differential analysis and explain where the complicated statistics come from.

The Central Limit Theorem is a pillar of statistics. We can apply the proof of the CLT to understand how different estimators converge in distribution with large sample sizes.

Mathematical notation is a signature of math. Almost anyone can recognize it instantly, even if they don't know what it is. I want to talk a bit of why notation is useful, why it can be confusing, and tackle some examples in statistics that are often confusin with some clear notation.

Some thoughts about what I'd like students taking the biostatistics course I'm TA'ing to take away from the class.

There are many reasons to be excited about scientific progress in the biological sciences, especially if you're a mathematician of almost any kind.

I want to highlight how clever the derivation of Tajima's statistic is, and a great idea he puts forward in his 1989 paper.