Every statistical model lives on a continuum between bias and variance, and navigating that tradeoff is one of the most critical modeling decisions you’ll ever make. Let’s start by getting clear on each term: Bias is the average error a model makes when fitting historical data. A high-bias model is too simple – it will underfit, miss patterns, and oversimplify dynamics that do matter. Think of a model that collapses all your observations to a single average: low variance, but consistently wrong. Variance, by contrast, is the additional error that shows up when a model is applied to unseen data. A high-variance model is overly sensitive to fluctuations in the training data. It fits the past extremely well – sometimes…