2007 Metrics Optimization Testing 
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.
Sisense
MAY 12, 2019
DevOps first came about in 2007-2008 to fix problems in the software industry and bring with it continuous improvement and greater efficiencies. In this case, insights that can be responded to in order to optimize a sequence or a larger process quickly. But is that really true? Is your DevOps movement doing what it was set out to do?
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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., Experiments, Parameters and Models At Youtube, the relationships between system parameters and metrics often seem simple — straight-line models sometimes fit our data well.
Occam's Razor
AUGUST 22, 2011
" ~ Web Metrics: "What is a KPI? " + Standard Metrics Revisited Series. "Engagement" Is Not A Metric, It's An Excuse. Defining a "Master Metric", + a Framework to Gain a Competitive Advantage in Web Analytics. The Awesome Power of Visualization 2 -> Death and Taxes 2007.
The Unofficial Google Data Science Blog
OCTOBER 7, 2015
A/B testing is used widely in information technology companies to guide product development and improvements. For questions as disparate as website design and UI, prediction algorithms, or user flows within apps, live traffic tests help developers understand what works well for users and the business, and what doesn’t.
The Unofficial Google Data Science Blog
JUNE 30, 2016
A naïve way to solve this problem would be to compare the proportion of buyers between the exposed and unexposed groups, using a simple test for equality of means. It should be noted that inverse probability weighting is not generally optimal (i.e., the curse of dimensionality). Here $c(x)$ is any function of $x$.
The Unofficial Google Data Science Blog
APRIL 19, 2021
To explain, let’s borrow a quote from Nate Silver’s The Signal and the Noise : One of the most important tests of a forecast — I would argue that it is the single most important one — is called calibration. Calibration and other considerations Calibration is a desirable property, but it is not the only important metric.
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