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For example, tests on a sign-up flow and a profile page could color the others results. redirected hereThis becomes hard to untangle,” says Shmukler. “Use statistical methods to alert your dashboard if theres a problematic interaction. It should allow you to choose to shut down one of the experiments and evaluate your remaining test. If you anticipate an issue before testing, consider using different selection criteria to route specific users into experiments to avoid those kinds of interactions altogether.” At LinkedIn, Shmukler and his team used sophisticated selection criteria to avoid interaction between experiments. “Wed select from users that weren’t in other experiments or sub-segment them to make sure that a segment was only in a single experiment at a time. You end up having to build more complex processes for selecting someone into an experiment then evaluating the experiment over time,” he says. As the complexity increases with concurrent experimentation, dashboards must become easier to interpret. “The best dashboards are the simple oneswhere it gives an indicative green or a red signalbut you still must read the results. You may want to run a red experiment for longer to gather and interpret more data,” says Shmukler.
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