For Yet Better Science

The past two decades have seen increasing interests in open data. Many scientists believe that the original research data should be properly organized and opened to the public and researchers throughout the world, and, once the open-data strategies are put into practice, the entire scientific research enterprise could be transformed. Driven by the trend of data sharing many platforms and repositories have been established. Universities, funding agencies and academic journals are also taking an active role in facilitating data sharing. In this forum discussion organized by National Science Review and chaired by Jianhui Li, panelists from diverse backgrounds who have all participated in the development of open data gathered together and talked about the recent progress and future directions of open data.  

Reproducibility has long been a tenet of science but has been challenging to achieve—we learned this the hard way when our old approaches proved inadequate to efficiently reproduce our own work. Here we describe how several free software tools have fundamentally upgraded our approach to collaborative research, making our entire workflow more transparent and streamlined. By describing specific tools and how we incrementally began using them for the Ocean Health Index project, we hope to encourage others in the scientific community to do the same—so we can all produce better science in less time.  

The first MIT Better Science Ideathon brought together teams of people involved in scientific research - including students, researchers, policy makers, publishers, and funders - to explore how the process of science can be improved. There was a focus on how open science can accelerate scientific progress through fostering collaboration and reproducibility and by reducing the barriers to learning.  

Tweets about Leonid Schneider.

Conducting research with human subjects can be difficult because of limited sample sizes and small empirical effects. We demonstrate that this problem can yield patterns of results that are practically indistinguishable from flipping a coin to determine the direction of treatment effects. We use this idea of random conclusions to establish a baseline for interpreting effect-size estimates, in turn producing more stringent thresholds for hypothesis testing and for statistical-power calculations. An examination of recent meta-analyses in psychology, neuroscience, and medicine confirms that, even if all considered effects are real, results involving small effects are indeed indistinguishable from random conclusions.

From leading-edge discoveries in astronomy, chemistry, and medicine, to inventing revolutionary devices, these women have made an indelible impact on our understanding of the world.  

The "New Statistics" emphasizes effect sizes, confidence intervals, meta-analysis, and the use of Open Science practices. We present 3 specific ways in which a New Statistics approach can help improve scientific practice: by reducing over-confidence in small samples, by reducing confirmation bias, and by fostering more cautious judgments of consistency. We illustrate these points through consideration of the literature on oxytocin and human trust, a research area that typifies some of the endemic problems that arise with poor statistical practice.