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Orthogonal predictions: follow‐up questions for suggestive data
Author(s) -
Walker Alexander M.
Publication year - 2010
Publication title -
pharmacoepidemiology and drug safety
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.023
H-Index - 96
eISSN - 1099-1557
pISSN - 1053-8569
DOI - 10.1002/pds.1929
Subject(s) - alternative hypothesis , statistical hypothesis testing , biological data , computer science , data science , econometrics , information retrieval , statistics , mathematics , bioinformatics , biology , null hypothesis
When a biological hypothesis of causal effect can be inferred, the hypothesis can sometimes be tested in the selfsame database that gave rise to the study data from which the hypothesis grew. Valid testing happens when the inferred biological hypothesis has scientific implications that predict new relations between observations already recorded. Testing for the existence of the new relations is a valid assessment of the biological hypothesis, so long as the newly predicted relations are not a logical correlate of the observations that stimulated the hypothesis in the first place. These predictions that lead to valid tests might be called ‘orthogonal’ predictions in the data, and stand in marked contrast to ‘scrawny’ hypotheses with no biological content, which predict simply that the same data relations will be seen in a new database. The Universal Data Warehouse will shortly render moot searches for new databases in which to test. Copyright © 2010 John Wiley & Sons, Ltd.