Inferential statistics , power estimates , and study design formalities continue to suppress biomedical innovation

Innovation is the direct intended product of certain styles in research, but not of others. Fundamental conflicts between descriptive vs inferential statistics, deductive vs inductive hypothesis testing, and exploratory vs pre-planned confirmatory research designs have been played out over decades, with winners and losers and consequences. Longstanding warnings from both academics and research-funding interests have failed to influence effectively the course of these battles. The NIH publicly studied and diagnosed important aspects of the problem a decade ago, resulting in outward changes in the grant review process but not a definitive correction. Specific reforms could deliberately abate the damage produced by the current overemphasis on inferential statistics, power estimates, and prescriptive study design. Such reform would permit a reallocation of resources to historically productive rapid exploratory efforts and considerably increase the chances for higher-impact research discoveries. We can profit from the history and foundation of these conflicts to make specific recommendations for administrative objectives and the process of peer review in decisions regarding research funding. © 2013 S. Kern “There is nothing more necessary to the man of science than its history, and the logic of discovery...: the way error is detected, the use of hypothesis, of imagination, the mode of testing.” – Lord Acton, quoted by Karl Popper (2) “The most striking feature of the normal research problems we have just encountered is how little they aim to produce major novelties, conceptual or phenomenal...everything but the most esoteric detail is known in advance, and the typical latitude of expectation is only somewhat wider...Normal science does not aim at novelties of fact or theory and, when successful, finds none.” – Thomas Kuhn (4) A pessimist, an optimist, an inferential statistician, and a descriptive statistician go into a bar. They order beers for everyone. When their own drinks arrive, the pessimist complains that his glass came half empty. The optimist expresses begrudging satisfaction that his is at least half full. The inferential statistician explains that one cannot exclude the null hypothesis, which holds that the half-full and halfempty glasses have been shorted by the same amount of beer. The descriptive statistician shakes his head, explaining that he saw the bartender switch to larger glasses after he had used all of the others. Academic research productivity is a subject of active research and discussion. Among the major determinants of research productivity is the research mix: the proportions of research devoted to novelty, incremental knowledge, or confirmatory research (5). It thus becomes critical to examine whether the objectives of biomedical research innovation are optimally served by current practices. This line of analysis leads through firmly established historical battlegrounds of publication and funding that remain crucial today. We must examine the key schisms in scientific and statistical philosophy, revisiting the fundamental questions of interest to Kuhn and Popper, Pearson and Tukey. We can then examine the administrative principles governing research policy decisions and consider specific recommendations to rebalance the research mix towards a specific goal of