2012 Experimentation Modeling Testing 
CIO Business Intelligence
MARCH 10, 2022
Fujitsu remains very much interested in the mainframe market, with a new model still on its roadmap for 2024, and a move under way to “shift its mainframes and UNIX servers to the cloud, gradually enhancing its existing business systems to optimize the experience for its end-users.”
The Unofficial Google Data Science Blog
APRIL 23, 2024
Experiments, Parameters and Models At Youtube, the relationships between system parameters and metrics often seem simple — straight-line models sometimes fit our data well. That is true generally, not just in these experiments — spreading measurements out is generally better, if the straight-line model is a priori correct.
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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
MAY 31, 2016
Similarly, we could test the effectiveness of a search ad compared to showing only organic search results. A geo experiment is an experiment where the experimental units are defined by geographic regions. Structure of a geo experiment A typical geo experiment consists of two distinct time periods: pretest and test.
O'Reilly on Data
JULY 25, 2023
I’m a professor who is interested in how we can use LLMs (Large Language Models) to teach programming. Swift Papers felt like a well-scoped project to test how well AI handles a realistic yet manageable real-world programming task. Setting the Stage: Who Am I and What Am I Trying to Build?
The Unofficial Google Data Science Blog
OCTOBER 12, 2017
Yet when we use these tools to explore data and look for anomalies or interesting features, we are implicitly formulating and testing hypotheses after we have observed the outcomes. We must correct for multiple hypothesis tests. Make experimentation cheap and understand the cost of bad decisions. We ought not dredge our data.
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