“Data dredging (also data fishing, data snooping, data butchery, and p-hacking) is the misuse of data analysis to find patterns in data that can be presented as statistically significant when in fact there is no real underlying effect. This is done by performing many statistical tests on the data and only paying attention to those that come back with significant results, instead of stating a single hypothesis about an underlying effect before the analysis and then conducting a single test for it.”
Source : Data dredging – Wikipedia
— Thomas Griessen (@ThomasGriessen) 12 novembre 2018
“Research at Netflix is aimed at improving various aspects of our business. Research applications span many areas including our personalization algorithms, content valuation, and streaming optimization. To maximize the impact of our research, we do not centralize research into a separate organization. Instead, we have many teams that pursue research in collaboration with business teams, engineering teams, and other researchers.
This allows for close partnerships between researchers and the business or engineering teams in each area. In addition, research that applies to the same methodological area or business area is shared and highlighted in discussion and debate forums to strengthen the work and its impact. These forums also serve to identify and motivate future research directions”.
Source : Netflix Research
« La jeune entreprise girondine, lauréate du prix de la mobilité Le Monde Smart Cities en 2016, est passée d’applications prédictives sur le vélo, à celles sur tous les types de mobilité. Elle mesure maintenant l’impact des aménagements urbains sur les piétons ».
Source : Qucit : « mathématiser la ville » pour prédire les comportements humains