Scientifically, these results are consistent with two particular studies recently published in neurorehabilitation journals. Yozbatiran et al. published a single case study describing the effects of robotic training on arm function after spinal cord injury. Interestingly improvements were only found in the non-dominant arm and hand.28 Similarly Boggio et al. studied the effects of transcranial direct current stimulation on hand function. They reported significant results were only found in the nondominant hand. They hypothesized that there could be greater neuroplasticity related to the underuse of the non-dominant hand.25This study offers a consistent profile of results across each item of the JTTHF with the non-dominant hand. The opportunities for research following this lead are wide open.”
Have the investigators presented adequate plans to address relevant biological variables, such as sex, for studies in vertebrate animals or human subjects? If the project involves human subjects and/or NIH-defined clinical research, are the plans to address 1) the protection of human subjects from research risks, and 2) inclusion (or exclusion) of individuals on the basis of sex/gender, race, and ethnicity, as well as the inclusion or exclusion of children, justified in terms of the scientific goals and research strategy proposed? In addition, for applications proposing clinical trials: Does the application adequately address the following, if applicable:Is the study design justified and appropriate to address primary and secondary outcome variable(s)/endpoints that will be clear, informative and relevant to the hypothesis being tested?
We need both observation- and hypothesis-driven research to get a full grasp of a field. At certain times—when a field is new or stuck on a question—observation may be more important. At other times, forming hypotheses and designing targeted studies may be more useful. The kind of observational work common in biology today—collecting large amounts of information—can help scientists see links between biological systems, like the gut and immune system, or genes and their transcripts in given cells, that they might otherwise have missed.
To shed light on the molecular mechanisms of those connections, we move from observation to hypothesis. If a large, data-driven study reveals that people with inflammatory bowel disease (IBD) have collections of bacteria in their gut that differ from people without IBD, we can begin to hypothesize the mechanism of the bacteria’s pathological effects. For instance, we could hypothesize that the high prevalence of bacteria A in IBD patients explains their disease. Then, we would design an experiment to test that hypothesis, studying whether mice with extra bacteria A get IBD. Moreover, we can modify the bacteria to determine the molecular determinants that trigger disease in those individuals or change one component of the immune system at a time and ask whether it can causally recapitulate the health status we’re interested in.
However, we now know that there’s no one right way to do science. Especially in extremely complex or fledgling fields, like the study of gut bacteria, many experiments stray from this formula. Often, we don’t have enough information to formulate a useful hypothesis, or the complexity of the system is not suitable for reductionist approaches. In those circumstances we ask a question that we have no best-guess answer for, and we collect lots of data to find an answer. This is observation-driven science.
To me, the best science happens not when we’re wedded to one way of doing things, but when we’re wedded to a topic that fascinates us and we can follow the research wherever it leads. There’s nothing wrong with a lab team doing observational study after observational study. They are still helping advance the science, and likely providing fodder for hypothesis-driven studies to come. After all, the best hypotheses always stem from a collection of important observations.