Scientists like good news: positive and significant findings impress the public and funders of the work. As a corollary, science tends to downplay studies with null or negative results — those that fail to confirm a preconceived hypothesis (usually in the form of being unable to find an expected relationship between variables or groups) or that demonstrate a new medication or behavioral intervention does not improve health. Negative or null findings are far less likely to be published than positive results. Unfortunately, this publication bias can mislead other researchers or the public.
Put this into the context of how scientists think of their work: how might one interpret a null finding, the failure in finding a predicted difference between two treatments or proposed causes? There are four possibilities. First, the researchers did not find what they and many others thought they had good reason to find. Second, the researchers did not find what only they thought they had good reason to find. Third, the researchers did not find what they only had a hunch they would find. Or, fourth, the researchers did not find what they had no good reason to expect but hoped they would anyway.
We make these distinctions because they can teach us a lot about science, how science is practiced, and how it can be better.
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