Transcription of Principled Statistical Inference in Data Science
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Principled Statistical Inference in Data Science
Principled Statistical Inference in data ScienceTodd A. KuffnerWashington University in St. Alastair YoungImperial College 5, 2017 AbstractWe discuss the challenges of Principled Statistical Inference in modern data principles are argued as key to achieving valid Statistical Inference , inparticular when this is performed after selecting a model from sample data and phrases: Statistical Inference ; principles ; data Science ; conditioning;post-selection Inference ; IntroductionIn recent times, even prominent figures in statistics have come to doubt the importance offoundational principles for data analysis. If a Statistical analysis is clearly shown to be effective at answering the questionsof interest, it gains nothing from being described as Principled . (Speed, 2016)The above statement was made by Terry Speed in the September 2016 IMS Bulletin. It isour primary purpose in this article to refute Professor Speed s assertion! We argue that aprincipled approach to Inference in the data Science context is essential, to avoid erroneousconclusions, in particular invalid statements about will be concerned here with Statistical Inference , specifically calculation and interpre-tation ofp values and construction of confidence intervals.
We discuss the challenges of principled statistical inference in modern data science. Conditionality principles are argued as key to achieving valid statistical inference, in particular when this is performed after selecting a model from sample data itself.
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