<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"
	xmlns:content="http://purl.org/rss/1.0/modules/content/"
	xmlns:wfw="http://wellformedweb.org/CommentAPI/"
	xmlns:dc="http://purl.org/dc/elements/1.1/"
	xmlns:atom="http://www.w3.org/2005/Atom"
	xmlns:sy="http://purl.org/rss/1.0/modules/syndication/"
	xmlns:slash="http://purl.org/rss/1.0/modules/slash/"
	>

<channel>
	<title>experimentation &#8211; Noise</title>
	<atom:link href="https://noise.getoto.net/tag/experimentation/feed/" rel="self" type="application/rss+xml" />
	<link>https://noise.getoto.net</link>
	<description>The collective thoughts of the interwebz</description>
	<lastBuildDate>Mon, 26 Aug 2024 15:46:24 +0000</lastBuildDate>
	<language>en-US</language>
	<sy:updatePeriod>
	hourly	</sy:updatePeriod>
	<sy:updateFrequency>
	1	</sy:updateFrequency>
	<generator>https://wordpress.org/?v=6.8.2</generator>
	<item>
		<title>Improve Your Next Experiment by Learning Better Proxy Metrics From Past Experiments</title>
		<link>https://noise.getoto.net/2024/08/26/improve-your-next-experiment-by-learning-better-proxy-metrics-from-past-experiments/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Mon, 26 Aug 2024 15:46:24 +0000</pubDate>
				<category><![CDATA[a-b-testing]]></category>
		<category><![CDATA[Data Science]]></category>
		<category><![CDATA[experimentation]]></category>
		<category><![CDATA[machine learning]]></category>
		<category><![CDATA[Statistics]]></category>
		<guid isPermaLink="false">https://medium.com/p/64c786c2a3ac</guid>

					<description><![CDATA[By Aurélien Bibaut, Winston Chou, Simon Ejdemyr, and Nathan KallusWe are excited to share our work on how to learn good proxy metrics from historical experiments at KDD 2024. This work addresses a fundamental question for technology companies and acade...]]></description>
		
		
		<enclosure url="" length="0" type="" />

			</item>
		<item>
		<title>Round 2: A Survey of Causal Inference Applications at Netflix</title>
		<link>https://noise.getoto.net/2024/06/06/round-2-a-survey-of-causal-inference-applications-at-netflix/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Thu, 06 Jun 2024 20:10:54 +0000</pubDate>
				<category><![CDATA[causal-inference]]></category>
		<category><![CDATA[Data Science]]></category>
		<category><![CDATA[experimentation]]></category>
		<category><![CDATA[Netflix]]></category>
		<category><![CDATA[Technology]]></category>
		<guid isPermaLink="false">https://medium.com/p/fd78328ee0bb</guid>

					<description><![CDATA[At Netflix, we want to ensure that every current and future member finds content that thrills them today and excites them to come back for more. Causal inference is an essential part of the value that Data Science and Engineering adds towards this miss...]]></description>
		
		
		<enclosure url="" length="0" type="" />

			</item>
		<item>
		<title>Sequential A/B Testing Keeps the World Streaming Netflix
Part 1: Continuous Data</title>
		<link>https://noise.getoto.net/2024/02/13/sequential-a-b-testing-keeps-the-world-streaming-netflixpart-1-continuous-data/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Tue, 13 Feb 2024 19:10:28 +0000</pubDate>
				<category><![CDATA[Data Science]]></category>
		<category><![CDATA[experimentation]]></category>
		<category><![CDATA[software-development]]></category>
		<category><![CDATA[software-testing]]></category>
		<category><![CDATA[Statistics]]></category>
		<guid isPermaLink="false">https://medium.com/p/cba6c7ed49df</guid>

					<description><![CDATA[Michael Lindon, Chris Sanden, Vache Shirikian, Yanjun Liu, Minal Mishra, Martin Tingley1. Spot the DifferenceCan you spot any difference between the two data streams below? Each observation is the time interval between a Netflix member hitting the play...]]></description>
		
		
		<enclosure url="" length="0" type="" />

			</item>
		<item>
		<title>How Product Teams Can Build Empathy Through Experimentation</title>
		<link>https://noise.getoto.net/2022/10/12/how-product-teams-can-build-empathy-through-experimentation/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Wed, 12 Oct 2022 20:56:33 +0000</pubDate>
				<category><![CDATA[experimentation]]></category>
		<category><![CDATA[experimentation-culture]]></category>
		<category><![CDATA[product-management]]></category>
		<guid isPermaLink="false">https://medium.com/p/6253603880a6</guid>

					<description><![CDATA[A conversation between Travis Brooks, Netflix Product Manager for Experimentation Platform, and George Khachatryan, OfferFit CEONote: I’ve known George for a little while now, and as we’ve talked a lot about the philosophy of experimentation, he kindly...]]></description>
		
		
		<enclosure url="" length="0" type="" />

			</item>
		<item>
		<title>Netflix: A Culture of Learning</title>
		<link>https://noise.getoto.net/2022/01/25/netflix-a-culture-of-learning/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Tue, 25 Jan 2022 16:23:45 +0000</pubDate>
				<category><![CDATA[ab-testing]]></category>
		<category><![CDATA[causal-inference]]></category>
		<category><![CDATA[decision-making]]></category>
		<category><![CDATA[experimentation]]></category>
		<guid isPermaLink="false">https://medium.com/p/394bc7d0f94c</guid>

					<description><![CDATA[Martin Tingley with Wenjing Zheng, Simon Ejdemyr, Stephanie Lane, Colin McFarland, Mihir Tendulkar, and Travis BrooksThis is the last post in an overview series on experimentation at Netflix. Need to catch up? Earlier posts covered the basics of A/B te...]]></description>
		
		
		<enclosure url="" length="0" type="" />

			</item>
		<item>
		<title>Experimentation is a major focus of Data Science across Netflix</title>
		<link>https://noise.getoto.net/2022/01/11/experimentation-is-a-major-focus-of-data-science-across-netflix/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Tue, 11 Jan 2022 16:09:13 +0000</pubDate>
				<category><![CDATA[ab-testing]]></category>
		<category><![CDATA[causal-inference]]></category>
		<category><![CDATA[decision-making]]></category>
		<category><![CDATA[experimentation]]></category>
		<guid isPermaLink="false">https://medium.com/p/f67923f8e985</guid>

					<description><![CDATA[Martin Tingley with Wenjing Zheng, Simon Ejdemyr, Stephanie Lane, Colin McFarland, Andy Rhines, Sophia Liu, Mihir Tendulkar, Kevin Mercurio, Veronica Hannan, Ting-Po LeeEarlier posts in this series covered the basics of A/B tests (Part 1 and Part 2 ), ...]]></description>
		
		
		<enclosure url="" length="0" type="" />

			</item>
		<item>
		<title>Building confidence in a decision</title>
		<link>https://noise.getoto.net/2021/11/15/building-confidence-in-a-decision/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Mon, 15 Nov 2021 16:20:05 +0000</pubDate>
				<category><![CDATA[ab-testing]]></category>
		<category><![CDATA[causal-inference]]></category>
		<category><![CDATA[decision-making]]></category>
		<category><![CDATA[experimentation]]></category>
		<guid isPermaLink="false">https://medium.com/p/8705834e6fd8</guid>

					<description><![CDATA[Martin Tingley with Wenjing Zheng, Simon Ejdemyr, Stephanie Lane, Michael Lindon, and Colin McFarlandThis is the fifth post in a multi-part series on how Netflix uses A/B tests to inform decisions and continuously innovate on our products. Need to catc...]]></description>
		
		
		<enclosure url="" length="0" type="" />

			</item>
		<item>
		<title>Interpreting A/B test results: false negatives and power</title>
		<link>https://noise.getoto.net/2021/10/26/interpreting-a-b-test-results-false-negatives-and-power/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Tue, 26 Oct 2021 15:45:24 +0000</pubDate>
				<category><![CDATA[ab-testing]]></category>
		<category><![CDATA[causal-inference]]></category>
		<category><![CDATA[decision-making]]></category>
		<category><![CDATA[experimentation]]></category>
		<guid isPermaLink="false">https://medium.com/p/6943995cf3a8</guid>

					<description><![CDATA[Martin Tingley with Wenjing Zheng, Simon Ejdemyr, Stephanie Lane, and Colin McFarlandThis is the fourth post in a multi-part series on how Netflix uses A/B tests to inform decisions and continuously innovate on our products. Need to catch up? Have a lo...]]></description>
		
		
		<enclosure url="" length="0" type="" />

			</item>
		<item>
		<title>Safe Updates of Client Applications at Netflix</title>
		<link>https://noise.getoto.net/2021/10/07/safe-updates-of-client-applications-at-netflix/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Thu, 07 Oct 2021 18:23:30 +0000</pubDate>
				<category><![CDATA[ab-testing]]></category>
		<category><![CDATA[Continuous Delivery]]></category>
		<category><![CDATA[experimentation]]></category>
		<category><![CDATA[metrics]]></category>
		<category><![CDATA[mobile-apps]]></category>
		<guid isPermaLink="false">https://medium.com/p/1d01c71a930c</guid>

					<description><![CDATA[By Minal MishraQuality of a client application is of paramount importance to global digital products, as it is the primary way customers interact with a brand. At Netflix, we have significant investments in ensuring new versions of our applications are...]]></description>
		
		
		<enclosure url="" length="0" type="" />

			</item>
		<item>
		<title>Interpreting A/B test results: false positives and statistical significance</title>
		<link>https://noise.getoto.net/2021/10/07/interpreting-a-b-test-results-false-positives-and-statistical-significance/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Thu, 07 Oct 2021 15:11:37 +0000</pubDate>
				<category><![CDATA[ab-testing]]></category>
		<category><![CDATA[causal-inference]]></category>
		<category><![CDATA[decision-making]]></category>
		<category><![CDATA[experimentation]]></category>
		<guid isPermaLink="false">https://medium.com/p/c1522d0db27a</guid>

					<description><![CDATA[Martin Tingley with Wenjing Zheng, Simon Ejdemyr, Stephanie Lane, and Colin McFarlandThis is the third post in a multi-part series on how Netflix uses A/B tests to inform decisions and continuously innovate on our products. Need to catch up? Have a loo...]]></description>
		
		
		<enclosure url="" length="0" type="" />

			</item>
		<item>
		<title>What is an A/B Test?</title>
		<link>https://noise.getoto.net/2021/09/22/what-is-an-a-b-test/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Wed, 22 Sep 2021 15:54:33 +0000</pubDate>
				<category><![CDATA[ab-testing]]></category>
		<category><![CDATA[causal-inference]]></category>
		<category><![CDATA[decision-making]]></category>
		<category><![CDATA[experimentation]]></category>
		<guid isPermaLink="false">https://medium.com/p/b08cc1b57962</guid>

					<description><![CDATA[Martin Tingley with Wenjing Zheng, Simon Ejdemyr, Stephanie Lane, and Colin McFarlandThis is the second post in a multi-part series on how Netflix uses A/B tests to inform decisions and continuously innovate on our products. See here for Part 1: Decisi...]]></description>
		
		
		<enclosure url="" length="0" type="" />

			</item>
		<item>
		<title>Decision Making at Netflix</title>
		<link>https://noise.getoto.net/2021/09/07/decision-making-at-netflix/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Tue, 07 Sep 2021 15:47:48 +0000</pubDate>
				<category><![CDATA[ab-testing]]></category>
		<category><![CDATA[causal-inference]]></category>
		<category><![CDATA[decision-making]]></category>
		<category><![CDATA[experimentation]]></category>
		<guid isPermaLink="false">https://medium.com/p/33065fa06481</guid>

					<description><![CDATA[Martin Tingley with Wenjing Zheng, Simon Ejdemyr, Stephanie Lane, and Colin McFarlandThis introduction is the first in a multi-part series on how Netflix uses A/B tests to make decisions that continuously improve our products, so we can deliver more jo...]]></description>
		
		
		<enclosure url="" length="0" type="" />

			</item>
		<item>
		<title>A Day in the Life of an Experimentation and Causal Inference Scientist @ Netflix</title>
		<link>https://noise.getoto.net/2021/03/03/a-day-in-the-life-of-an-experimentation-and-causal-inference-scientist-netflix/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Wed, 03 Mar 2021 00:45:43 +0000</pubDate>
				<category><![CDATA[ab-testing]]></category>
		<category><![CDATA[causal-inference]]></category>
		<category><![CDATA[Data Science]]></category>
		<category><![CDATA[experimentation]]></category>
		<guid isPermaLink="false">https://medium.com/p/388edfb77d21</guid>

					<description><![CDATA[Stephanie Lane, Wenjing Zheng, Mihir TendulkarSource credit: NetflixWithin the rapid expansion of data-related roles in the last decade, the title Data Scientist has emerged as an umbrella term for myriad skills and areas of business focus. What does t...]]></description>
		
		
		<enclosure url="" length="0" type="" />

			</item>
		<item>
		<title>Netflix at MIT CODE 2020</title>
		<link>https://noise.getoto.net/2020/12/16/netflix-at-mit-code-2020/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Wed, 16 Dec 2020 19:38:44 +0000</pubDate>
				<category><![CDATA[a-b-testing]]></category>
		<category><![CDATA[causal-inference]]></category>
		<category><![CDATA[Data Science]]></category>
		<category><![CDATA[experimentation]]></category>
		<guid isPermaLink="false">https://medium.com/p/ad3745525218</guid>

					<description><![CDATA[Martin TingleyIn November, Netflix was a proud sponsor of the 2020 Conference on Digital Experimentation (CODE), hosted by the MIT Initiative on the Digital Economy. As well as providing sponsorship, Netflix data scientists were active participants, wi...]]></description>
		
		
		<enclosure url="" length="0" type="" />

			</item>
		<item>
		<title>Key Challenges with Quasi Experiments at Netflix</title>
		<link>https://noise.getoto.net/2020/09/02/key-challenges-with-quasi-experiments-at-netflix/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Tue, 01 Sep 2020 21:54:58 +0000</pubDate>
				<category><![CDATA[ab-testing]]></category>
		<category><![CDATA[Data Science]]></category>
		<category><![CDATA[experimentation]]></category>
		<guid isPermaLink="false">https://medium.com/p/89b4f234b852</guid>

					<description><![CDATA[Kamer Toker-Yildiz, Colin McFarland, Julia GlickAt Netflix, when we can’t run A/B experiments we run quasi experiments! We run quasi experiments with various objectives such as non-member experiments focusing on acquisition, member experiments focusing...]]></description>
		
		
		<enclosure url="" length="0" type="" />

			</item>
	</channel>
</rss>

<!--
Performance optimized by W3 Total Cache. Learn more: https://www.boldgrid.com/w3-total-cache/

Object Caching 41/201 objects using Memcached
Page Caching using Disk: Enhanced 
Lazy Loading (feed)
Database Caching using Memcached

Served from: noise.getoto.net @ 2025-12-06 10:06:15 by W3 Total Cache
-->