A recurrent issue in our forum is applied analysis questions. Not only are there ample examples in the CV network, but it comes up in Meta repeatedly. I think questions of the nature are appropriate for this network, but I would say less than 1% of these questions are detailed enough to get appropriate answers. It reflects poorly on the community. How can CV improve this situation?
I propose we create an analysis tag to guide users on what constitutes adequate information to get high quality answers. If users are not able to conform the question to these standards, it should be closed for being "too broad"-- since it forces us to answer in an abstract, turnkey manner that is too nebulous to be useful (if we can answer at all). This is exactly the approach that we take with self-study which is similarly problematic. With self-study, our site policies have improved our answers and questions dramatically.
There is no existing analysis tag.
I propose that the analysis tag be written in the following fashion:
Analysis: For questions dealing with the applied analysis of a specific dataset or design of experiment. Posters may be either statisticians or non experts in the field. Questions need to address the following issues:
- The field of application (e.g. biostatistics, econometrics, social sciences...)
- A basic statement of hypothesis or hypotheses
- A detailed description of the dataset including the sampling method used, measures collected, the measuring methodology (e.g. mass spectrometry, questionnaire, physical exam)
- Basic proposed data analysis plan
- Precise description of the problems encountered
If others concur, I am happy to resurrect some unanswered "analysis" questions and post in a comment that the user should consult the "analysis" tag to improve the quality of the post. If it is not addressed in a week, we can close it.
As a statistician, working with other scientists to discover what they want to do is my favorite part of the job. At work I encourage others to follow this discipline. What might we add to this tag to make it better applicable in this circumstance and invite more non-statistical researchers to participate in the forum?