Hypothesis Testing

Hypothesis testing is the practice of formulating a clear, testable claim — “adding examples of cautious refusals to the training data will reduce over-refusal on medical topics” — and then designing an experiment to evaluate whether that claim is supported by evidence. In AI behavioral work, this means defining what you expect to see if the hypothesis is true, running the experiment, collecting data, and honestly assessing whether the results confirm or disconfirm the prediction. For behavior architects, hypothesis testing is the discipline that separates principled iteration from random experimentation — when you make changes, you should be able to say what you expected to happen and whether that expectation was met. Without this discipline, it’s easy to mistake noise for signal and mistake correlation for causation.