Summary:
- The article discusses the practical challenges of statistical modeling, specifically focusing on the iterative and non-linear nature of the research process ("fits and starts").
- It highlights the importance of workflow management, data cleaning, and the necessity of re-evaluating model assumptions when initial results appear counterintuitive or problematic.
- The author emphasizes that scientific progress in statistics is rarely a straight line and often requires backtracking to refine methodology before arriving at robust conclusions.