models & measurements
There are two main analyses performed in a scientific context: measurement and model selection.
Here, we explore the topic of measurement. We see that models of the data are always a part of measurement, although we show that the presence of a model is sometimes 'transparent' to our calculations. We then develop a measurement algorithm that allows measurements to be made for a wide range of models. The utility of this algorithm is demonstrated in a series of worked examples, such as measuring rates (death rates, recovery rates, etc.), rate differences (differential recovery), times and time intervals (reaction time and qrs duration), means, variances, straight-line slopes, exponential decay, and stimulus detectability (d-prime).