Statistical Practice Is Not a Spectator Sport

The Statistical Inquiry CycleBoston University is to be congratulated on the innovative M.S. in Statistical Practice (MSSP) program described by Kolaczyk et al. ("Statistics Practicum: Placing 'Practice' at the Center of Data Science Education," this issue). Reading the description of their program brought to mind a 25-year-old quote from the American Association of Higher Education Bulletin:Learning is not a spectator sport.Students do not learn much just sitting in classes listening to teachers, memorizing prepackaged assignments, and spitting out answers.They must talk about what they are learning, write reflectively about it, relate it to past experiences, and apply it to their daily lives.They must make what they learn part of themselves.(Chickering & Ehrmann, 1996).The idea behind this generic quote can be adapted to learning statistical practice.Before doing so, I will outline one way to think about statistical practice-the statistical inquiry cycle described by Wild et al. (2018) in the International Handbook of Research in Statistics Education.The details appear to have originated with MacKay and Oldford (2000) and were described under a different name by Wild and Pfannkuch (1999).The statistical inquiry cycle includes five steps, with the acronym PPDAC.The steps of the statistical inquiry cycle are:It should be clear from reading these steps that they are interrelated.The beauty of a program like Boston University's MSSP is that students are able to experience these steps as a cohesive whole.Using these ideas, the spectator sport quote as applied to learning statistical practice might read as follows.Problem: You can't solve a problem without first defining what it is, and you shouldn't try to solve a problem without questioning why you are doing it.This step should include a discussion of such issues as the ethical implications of possible outcomes and how the problem fits with existing knowledge.Plan: This step includes such issues as possible sources of data, sampling design, what variables to measure, possible confounding variables, potential analyses, plans for data management and data privacy, and power analyses.Data: This is the stage at which the data collection part of the plan is executed, and the data are prepared for analysis.Analysis: At this stage the inquiry process should cycle back to the "problem" step, to make sure the analyses are appropriate for answering the questions of interest.It also should cycle back to the "plan" step to make sure the analysis doesn't turn into an unethical data fishing exercise.Conclusion: The conclusion stage includes more than simply reporting results.Discussion should cycle back to the problem defined in the first stage, the strengths and weaknesses of the plans in the second stage, any problems encountered in the data stage, and a summary of the analysis stage.