For questions dealing with the applied analysis of a specific dataset or design of experiment. Posts tagged analysis are requesting statistical consulting assistance from the network. Questions tagged analysis need to be phrased appropriately.

For questions dealing with the applied analysis of a specific dataset or design of experiment. Posts tagged analysis are requesting statistical consulting assistance from the network. Questions tagged analysis need to be phrased appropriately.

Give your post an informative title for visibility

This should allude to the nature of the hypothesis being tested, the field of study, asking about the appropriateness or relative benefits of certain analytic methods. Pose it as a question to help secure the analysis tag quickly.

Do not use partial or incomplete sentences. Be as detailed as you can with a 50-70 char max title.

Examples of good titles:

  • Biostatistics: can I use Cox Models to predict failure outcomes in a cohort of heart transplant recipients?
  • Economics: How do I assess stationarity for ARMA models for index performance metrics?
  • Neuroscience: How do I estimate neural spiking models based on cortical EEGs?
  • Social Science: How do I assess differential item functioning in a cognitive tool for elderly veterans?

Examples of bad titles:

  • What test do I use?
  • Bioinformatics microarrays
  • Does bootstrapping work for my data?

Be as detailed as possible with the body of the question:

Include the following information:

  1. The field of application (e.g. biostatistics, econometrics, social sciences...)
  2. A basic statement of hypothesis or hypotheses
  3. A detailed description of the dataset including the sample size, sampling method(s) used, variables collected, the measuring methodology (e.g. mass spectrometry, questionnaire, physical exam), missingness, and variable coding.
  4. Basic proposed data analysis plan
  5. Precise description of the problems encountered

Explain the jargon in your field. Write out all acronyms and shorthands unless they are obvious.

Be clear about the answer you are looking for

Details on how to implement the suggestions here in statistical software are not appropriate for this site. Do not tag such questions with R, SAS, or SPSS as they will likely be migrated to stack overflow.

Typical statistical solutions that you may expect here are:

  • Suggestions of particular statistical tests to evaluate researcher hypotheses
  • Graphical or numerical summaries that may enhance a particular analysis
  • Interpretation of results including p-values, confidence intervals, credibility regions, or Bayes factors for non-researchers or other statisticians
  • Interpretation of statistical output
  • Correctness of proposed methods or the appropriateness of secondary analyses if certain assumptions are violated
  • Being unsure of the assumptions necessary for a particular test?
  • How to handle correlated observations?
  • Ascertaining adequate power or sample size for particular methods
  • Detailed specifications on how to control for blocking, stratum, or confounding / mediating factors in analyses
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