AutoDiscovery-Exploring Complex Relationships for Scientific Discovery

#artificialintelligence 

More detailed analysis would follow from initial discoveries of interesting and significant parameter correlations within complex high-dimensional data. An article was recently published in Nature on "Statistical Errors – p Values, the Gold Standard of Statistical Validity, Are Not as Reliable as Many Scientists Assume" (by Regina Nuzzo, Nature, 506, 150-152, 2014). In this article, Columbia University statistician Andrew Gelman states that instead of doing multiple separate small studies, "researchers would first do small exploratory studies and gather potentially interesting findings without worrying too much about false alarms. Then, on the basis of these results, the authors would decide exactly how they planned to confirm the findings." In other words, a disciplined scientific methodology that includes both exploratory and confirmatory analyses can be documented within an open science framework (e.g., https://osf.io)