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Motor learning is a central focus of several disciplines including kinesiology, neuroscience and rehabilitation. However, given the different traditions of these fields, this interdisciplinarity can be a challenge when trying to interpret evidence and claims from motor learning experiments. To address this issue, we offer a set of ten guidelines for designing motor learning experiments starting from task selection to data analysis, primarily from the viewpoint of running lab-based experiments. The guidelines are not intended to serve as rigid rules, but instead to raise awareness about key issues in motor learning. We believe that addressing these issues can increase the robustness of work in the field and its relevance to the real-world. 

Missing data incidents are common in experimental studies of motor learning and development. Inadequate handling of missing data may lead to serious problems, such as addition of bias, reduction in power, and so on. Thus, this study aimed to conduct a systematic review of the past (2007) and present (2017) practices used for reporting and analyzing missing data in motor learning and development. For this purpose, the authors reviewed 309 articles from five journals focusing on motor learning and development studies and published in 2007 and 2017. The authors carefully reviewed each article using a six-stage review process to assess the reporting and analyzing practices. Reporting of missing data along with reasons for their presence was consistently high across time, which slightly increased in 2017. Researchers predominantly used older methods (mainly deletion) for analysis, which only showed a small increase in the use of newer methods in 2017. While reporting practices were exemplary, missing data analysis calls for serious attention. Improvements in missing data handling may have the merit to address some of the major issues, such as underpowered studies, in motor learning and development.