In the world of high-level policy debate, the air is often filled with “spread,” the practice of speaking at upwards of 300 words per minute to maximize the density of arguments. To an outsider, it sounds like a chaotic blur of rhetoric and adrenaline. However, beneath this high-velocity exterior lies a rigid, almost mechanical system of logical dependencies. A Disadvantage (sidenote: An argument brought up by the negative team highlighting the consequence resulting from the passage of the plan.

In the world of data science, dealing with uncertainty is a constant challenge. Predicting outcomes, modeling trends, or making decisions based on incomplete data all boil down to one thing: how confident are we in the predictions we make? This is where Bayesian Inference comes into play. It provides a framework for updating our beliefs about a hypothesis as we gather new evidence, allowing us to model uncertainty in a probabilistic and flexible manner.