Experimentation Interactive Metrics Uncertainty 
Occam's Razor
APRIL 8, 2013
To win in business you need to follow this process: Metrics > Hypothesis > Experiment > Act. We are far too enamored with data collection and reporting the standard metrics we love because others love them because someone else said they were nice so many years ago. That metric is tied to a KPI.
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., Crucially, it takes into account the uncertainty inherent in our experiments. Here, $X$ is a vector of tuning parameters that control the system's operating characteristics (e.g.
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The Unofficial Google Data Science Blog
DECEMBER 14, 2021
by AMIR NAJMI & MUKUND SUNDARARAJAN Data science is about decision making under uncertainty. Some of that uncertainty is the result of statistical inference, i.e., using a finite sample of observations for estimation. But there are other kinds of uncertainty, at least as important, that are not statistical in nature.
The Unofficial Google Data Science Blog
JANUARY 14, 2016
Unlike experimentation in some other areas, LSOS experiments present a surprising challenge to statisticians — even though we operate in the realm of “big data”, the statistical uncertainty in our experiments can be substantial. We must therefore maintain statistical rigor in quantifying experimental uncertainty.
Domino Data Lab
JUNE 23, 2019
Skomoroch proposes that managing ML projects are challenging for organizations because shipping ML projects requires an experimental culture that fundamentally changes how many companies approach building and shipping software. Another pattern that I’ve seen in good PMs is that they’re very metric-driven.
The Unofficial Google Data Science Blog
APRIL 16, 2019
by MICHAEL FORTE Large-scale live experimentation is a big part of online product development. This means a small and growing product has to use experimentation differently and very carefully. This blog post is about experimentation in this regime. Such decisions involve an actual hypothesis test on specific metrics (e.g.
The Unofficial Google Data Science Blog
OCTOBER 21, 2015
It is important to make clear distinctions among each of these, and to advance the state of knowledge through concerted observation, modeling and experimentation. These quasi-explanations usually involve large, real effects and interactions so complex that arguments based on them are often non-falsifiable.
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