ABSTRACT
We learn from data, both experimental and observational data. Scientists propose hypotheses about the underlying mechanism of the subject under study. These hypotheses are then tested by comparing the logic consequences of these hypotheses to the observed data. A hypothesis is a model about the realworld. The logical consequence is what the model predicts. Comparing model predictions and observations is to decide whether the proposed model is likely to produce the observed data. A positive result provides evidence supporting the proposed model, while a negative result is evidence against the model. This process is a typical scientific inference process. The proper handling of the uncertainty in data and in the model is often the difficulty in this process. The role of statistics in scientific research is to provide quantitative tools for bridging the gap between observed data and proposed models.
