by Substantive relevance — not ‘clever’ design — is what matters most in science [embedded content] If anything, Snow’s path-breaking research underlines how important it is not to equate science with statistical calculation. And that the value of ‘as-if’ random interventions and experiments ultimately depend on the degree to which they if shed light on substantive and interesting scientific questions. All science entail human judgement, and using statistical models doesn’t relieve us of that necessity. And we should never forget that the underlying parameters we use when performing statistical tests are model constructions. And if the model is wrong, the value of our calculations is nil. As ‘shoe-leather researcher’ David Freedman wrote in Statistical
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Lars Pålsson Syll considers the following as important: Statistics & Econometrics
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Substantive relevance — not ‘clever’ design — is what matters most in science
If anything, Snow’s path-breaking research underlines how important it is not to equate science with statistical calculation. And that the value of ‘as-if’ random interventions and experiments ultimately depend on the degree to which they if shed light on substantive and interesting scientific questions.
All science entail human judgement, and using statistical models doesn’t relieve us of that necessity. And we should never forget that the underlying parameters we use when performing statistical tests are model constructions. And if the model is wrong, the value of our calculations is nil. As ‘shoe-leather researcher’ David Freedman wrote in Statistical Models and Causal Inference:
I believe model validation to be a central issue. Of course, many of my colleagues will be found to disagree. For them, fitting models to data, computing standard errors, and performing significance tests is “informative,” even though the basic statistical assumptions (linearity, independence of errors, etc.) cannot be validated. This position seems indefensible, nor are the consequences trivial. Perhaps it is time to reconsider.