My text Intuitive Biostatistics is a nonmathematical explanation of conventional statistics. Chapter 3 explains the basic mindset of statistics as analyzing a sample to make inferences from a population, or to fit a scientifically sensible model to find parameters to understand and compare.
For the next (fourth) edition, I want to add a short section explaining that there are other mindsets used in data analysis. I'd welcome a critique. Thanks!
This chapter has discussed the basic idea of statistics as used in many scientific and clicnical situations. You can think of this approach as using data from a sample to make inferences about a population. Alternatively, you can think about fitting and comparing understandable models in order to obtain parameter values that can be interpreted and compared.
Sometimes statistics is used with an entirely different goal: to predict future events, or to find patterns in data. In these situations, it is not always necessary to think about samples and populations, or to think about a model that expresses a scientific idea. Instead the goal is to simply find an equation or algorithm that makes reasonably correct predictons. Enter one set of data to obtain a rule for making a prediction, and evalutate these predictions with another set of data. This approach goes by many names including machine learning, neural networks, data mining and predictive modelling. Those terms don’t all mean exactly the same thing, but all describe approaches to data analysis that use an approach not covered in this chapter or anywhere in this book.