Big Data Experimentation Statistics Uncertainty 
The Unofficial Google Data Science Blog
JANUARY 14, 2016
by AMIR NAJMI Running live experiments on large-scale online services (LSOS) is an important aspect of data science. Because individual observations have so little information, statistical significance remains important to assess. We must therefore maintain statistical rigor in quantifying experimental uncertainty.
IBM Big Data Hub
APRIL 18, 2024
LLMs like ChatGPT are trained on massive amounts of text data, allowing them to recognize patterns and statistical relationships within language. The AGI would need to handle uncertainty and make decisions with incomplete information.
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The Unofficial Google Data Science Blog
JULY 22, 2020
For example, imagine a fantasy football site is considering displaying advanced player statistics. 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. One reason to do ramp-up is to mitigate the risk of never before seen arms.
Occam's Razor
APRIL 8, 2013
Remember that the raw number is not the only important part, we would also measure statistical significance. Airbnb had enough data points to be confident in their results. They might deal with uncertainty, but they're not random. The result? The properties with professional photography had 2-3 times the number of bookings!
The Unofficial Google Data Science Blog
FEBRUARY 29, 2016
In this post we explore why some standard statistical techniques to reduce variance are often ineffective in this “data-rich, information-poor” realm. Despite a very large number of experimental units, the experiments conducted by LSOS cannot presume statistical significance of all effects they deem practically significant.
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. Yet, this challenge is not insurmountable. for what is and isn’t possible) to address these challenges.
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. Note also that this account does not involve ambiguity due to statistical uncertainty. We sliced and diced the experimental data in many many ways.
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