A hypothesis is a liability
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EDITORIAL
Open Access
A hypothesis is a liability Itai Yanai1* and Martin Lercher2* * Correspondence: itai.yanai@ nyulangone.org; martin.lercher@ hhu.de 1 Institute for Computational Medicine, NYU Langone Health, New York, NY 10016, USA 2 Institute for Computer Science & Department of Biology, Heinrich Heine University, 40225 Düsseldorf, Germany
“ ‘When someone seeks,’ said Siddhartha, ‘then it easily happens that his eyes see only the thing that he seeks, and he is able to find nothing, to take in nothing. [...] Seeking means: having a goal. But finding means: being free, being open, having no goal.’ ” Hermann Hesse There is a hidden cost to having a hypothesis. It arises from the relationship between night science and day science, the two very distinct modes of activity in which scientific ideas are generated and tested, respectively [1, 2]. With a hypothesis in hand, the impressive strengths of day science are unleashed, guiding us in designing tests, estimating parameters, and throwing out the hypothesis if it fails the tests. But when we analyze the results of an experiment, our mental focus on a specific hypothesis can prevent us from exploring other aspects of the data, effectively blinding us to new ideas. A hypothesis then becomes a liability for any night science explorations. The corresponding limitations on our creativity, self-imposed in hypothesis-driven research, are of particular concern in the context of modern biological datasets, which are often vast and likely to contain hints at multiple distinct and potentially exciting discoveries. Night science has its own liability though, generating many spurious relationships and false hypotheses. Fortunately, these are exposed by the light of day science, emphasizing the complementarity of the two modes, where each overcomes the other’s shortcomings.
The gorilla experiment Many of us recall the famous selective attention experiment, where subjects watch a clip of students passing a basketball to each other [3, 4]. If you have not seen it, we recommend watching it before continuing to read [5]. As you watch the two teams in action, your task is to count the number of passes made by the team in white. About halfway through, a person dressed up as a gorilla enters the foreground. The gorilla pauses in the center, pounding its chest with its fists, before exiting to the opposite side of the frame. Surprisingly, half of us completely miss the gorilla, as we are focused on counting passes, even though hardly anyone overlooks it when simply watching the clip without the assignment. We wondered if a similar process occurs when we analyze a dataset. Would the mental focus on a specific hypothesis prevent us from making a discovery? To test this, we made up a dataset and asked students to analyze it [6]. We described the dataset as containing © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as
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