Hypothesis testing: A Historical Perspective
From Classical Statistics to Adaptive Inference Hypothesis testing used to be a straightforward affair. The rules were clear, the assumptions tight. A neatly defined null hypothesis, a p-value below 0.05, and a conclusion that held weight. It worked well in controlled experiments, in a world where datasets were small and well-behaved. But the world changed. The data grew messy, streaming in from sensors, clickstreams, and real-time systems. The clean lines of classical statistics started to blur.
From Classical Statistics to Adaptive Inference
Hypothesis testing used to be a straightforward affair. The rules were clean, the assumptions tight. A neatly defined null hypothesis, a p-value below 0.05, and a conclusion that held weight. It worked well in controlled experiments, in a world where datasets were small and well-behaved. But the world changed. The data grew messy, streaming in from sensors, clickstreams, and real-time systems. The clean lines of classical statistics started to blur.
