2007 Experimentation Optimization Testing 
The Unofficial Google Data Science Blog
APRIL 23, 2024
If the relationship of $X$ to $Y$ can be approximated as quadratic (or any polynomial), the objective and constraints as linear in $Y$, then there is a way to express the optimization as a quadratically constrained quadratic program (QCQP). However, joint optimization is possible by increasing both $x_1$ and $x_2$ at the same time.
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.
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Occam's Razor
APRIL 8, 2013
Sometimes, we escape the clutches of this sub optimal existence and do pick good metrics or engage in simple A/B testing. You're choosing only one metric because you want to optimize it. Testing out a new feature. Identify, hypothesize, test, react. You don’t need a beautiful beast to go out and test.
Occam's Razor
SEPTEMBER 19, 2011
We have to do Search Engine Optimization. Then they isolated regions of the country (by city, zip, state, dma pick your fave) into test and control regions. People in the test regions will participate in our hypothesis testing. So for variation #3, no catalogs or email were sent to the customers in the test group.
Occam's Razor
AUGUST 22, 2011
The Awesome Power of Visualization 2 -> Death and Taxes 2007. Five Reasons And Awesome Testing Ideas. Lab Usability Testing: What, Why, How Much. Build A Great Web Experimentation & Testing Program. Experimentation and Testing: A Primer. Interviewing Tip: Stress Test Critical Thinking.
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. 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.
The Unofficial Google Data Science Blog
JULY 22, 2020
Another reason to use ramp-up is to test if a website's infrastructure can handle deploying a new arm to all of its users. The website wants to make sure they have the infrastructure to handle the feature while testing if engagement increases enough to justify the infrastructure. We offer two examples where this may be the case.
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