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b'When data scientists run experiments, like A/B tests, it\\u2019s really easy to plan on a period of a few days to a few weeks for collecting data. The thing is, the change that\\u2019s being evaluated might have effects that last a lot longer than a few days or a few weeks\\u2014having a big sale might increase sales this week, but doing that repeatedly will teach customers to wait until there\\u2019s a sale and never buy anything at full price, which could ultimately drive down revenue in the long term. Increasing the volume of ads on a website might lead people to click on more ads in the short term, but in the long term they\\u2019ll be more likely to visually block the ads out and learn to ignore them. But these long-term effects aren\\u2019t apparent from the short-term experiment, so this week we\\u2019re talking about a paper from Google research that confronts the short-term vs. long-term tradeoff, and how to measure long-term effects from short-term experiments. \\n\\nRelevant links:\\nhttps://research.google/pubs/pub43887/'