Data Science Knowledge Discovery Metrics Uncertainty 
The Unofficial Google Data Science Blog
JANUARY 14, 2016
by AMIR NAJMI Running live experiments on large-scale online services (LSOS) is an important aspect of data science. We must therefore maintain statistical rigor in quantifying experimental uncertainty. In this post we explore how and why we can be “ data-rich but information-poor ”.
The Unofficial Google Data Science Blog
FEBRUARY 29, 2016
The result is that experimenters can’t afford to be sloppy about quantifying uncertainty. These typically result in smaller estimation uncertainty and tighter interval estimates. Variance reduction through conditioning Suppose, as an LSOS experimenter, you find that your key metric varies a lot by country and time of day.
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