2015 Experimentation Measurement Risk 
The Unofficial Google Data Science Blog
APRIL 23, 2024
the weight given to Likes in our video recommendation algorithm) while $Y$ is a vector of outcome measures such as different metrics of user experience (e.g., Taking measurements at parameter settings further from control parameter settings leads to a lower variance estimate of the slope of the line relating the metric to the parameter.
Smart Data Collective
FEBRUARY 4, 2020
In a report on the failure rates of drug discovery efforts between 2013 and 2015, Richard K. Without better methodology, difficult-to-treat and ill-understood conditions and diseases are at risk of staying that way. Unfortunately, a substantial number of clinical trials fails in these two Phases.
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The Unofficial Google Data Science Blog
JULY 22, 2020
One reason to do ramp-up is to mitigate the risk of never before seen arms. A ramp-up strategy may mitigate the risk of upsetting the site’s loyal users who perhaps have strong preferences for the current statistics that are shown. For example, imagine a fantasy football site is considering displaying advanced player statistics.
Domino Data Lab
FEBRUARY 4, 2019
Spoiler alert: a research field called curiosity-driven learning is emerging at the nexis of experimental cognitive psychology and industry use cases for machine learning, particularly in gaming AI. The ability to measure results (risk-reducing evidence). and dig into details about where science meets rhetoric in data science.
The Unofficial Google Data Science Blog
MAY 31, 2016
It is important that we can measure the effect of these offline conversions as well. Panel studies make it possible to measure user behavior along with the exposure to ads and other online elements. Let's take a look at larger groups of individuals whose aggregate behavior we can measure. days or weeks).
Occam's Razor
JANUARY 9, 2012
Most companies are astonishingly blasé about data and possibilities of measurement. " Sad, unimaginative measurements of their sad, unimaginative campaigns. We don't take risk and try things, imaginative (possibly glorious) things, because we believe the price of failure is so high. AND you can control for risk!
The Unofficial Google Data Science Blog
JUNE 30, 2016
In an ideal world, experimentation through randomization of the treatment assignment allows the identification and consistent estimation of causal effects. See Hainmueller (2012), and the work of Zhao & Percival (2015) for more details on how this optimization problem is solved, and for further discussion. R package version 2.0-15.
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