- The litany of problems with p-values - catalog of all the problems I can think of
- Matching vs. covariate adjustment (see below from Arne Warnke)
- Statistical strategy for propensity score modeling and usage
- Analysis of change: why so many things go wrong
- What exactly is a type I error and should we care? (analogy: worrying about the chance of a false positive diagnostic test vs. computing current probability of disease given whatever the test result was). Alternate title: Why Clinicians' Misunderstanding of Probabilities Makes Them Like Backwards Probabilities Such As Sensitivity, Specificity, and Type I Error.
- Forward vs. backwards probabilities and why forward probabilities serve as their own error probabilities (we have been fed backwards probabilities such as p-values, sensitivity, and specificity for so long it's hard to look forward)
- What is the full meaning of a posterior probability?
- Posterior probabilities can be computed as often as desired
- Statistical critiques of published articles in the biomedical literature
- New dynamic graphics capabilities using R plotly in the R Hmisc package: Showing more by initially showing less
- Moving from pdf to html for statistical reporting
- Is machine learning statistics or computer science?
- Sample size calculation: Is it voodoo?
- Difference between Bayesian modeling and frequentist inference
- Proper accuracy scoring rules and why improper scores such as proportion "classified" "correctly" give misleading results.
I think in your ‘philosophy’, this would belong to the point “Preserve all the information in the data”.
Comment: Nice to know this exists but I've never seen a paper that used matching attempt to explore interactions.