The way forward — discard 90% of the data!

LARS P. SYLL

Could it be better to discard 90% of the reported research? Surprisingly, the answer is yes to this statistical paradox. This paper has shown how publication selection can greatly distort the research record and its conventional summary statistics. Using both Monte Carlo simulations and actual research examples, we show how a simple estimator, which uses only 10 percent of the reported research reduces publication bias and improves efficiency over conventional summary statistics that use all the reported research.

gonefishing1The average of the most precise 10 percent, ‘Top10,’ of the reported estimates of a given empirical phenomenon is often better than conventional summary estimators because of its heavy reliance on the reported estimate’s precision (i.e., the inverse of the estimate’s standard error). When estimates are chosen, in part, for their statistical significance, studies cursed with imprecise estimates have to engage in more intense selection from among alternative statistical techniques, models, data…

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