Detecting interference: An A/B test of A/B tests

At LinkedIn, most decisions are made using experiments. When we want to decide between two features, we test them against each other in the real world: we give feature A to a random set of members, feature B to another set, and we compare the results. Are users of feature A more engaged? Do they have a better experience with our products? If so, feature A wins.

Detecting interference: An A/B test of A/B tests Skip to main content LinkedIn respects your privacy LinkedIn and 3rd parties use essential and non-essential cookies to provide, secure, analyze and improve our Services, and to show you relevant ads (including professional and job ads ) on and off LinkedIn. Learn more in our Cookie Policy . Select Accept to consent or Reject to decline non-essential cookies for this use. You can update your choices at any time in your settings . Accept Reject A/B Testing/Experimentation Detecting interference: An A/B test of A/B tests Authored by Guillaume Sain

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