Your friend Sam is excited about the initial results of his statistical analysis. He says that the data show a positive correlation between the number of app downloads and the number of app review impressions. But he's not sure whether they would have downloaded it anyway without seeing the review. What response would be most helpful to Sam?ATrust the data. It's clear that that great review is the reason users are downloading the app.BYou could run an experiment to compare the behavior of users who didn't see the review with similar users who did. CRun an experiment to ask those who downloaded the app on their specific interests and reasons why they did itDDetermine the strength of the correlation statistics to evaluate its significance
Question
Your friend Sam is excited about the initial results of his statistical analysis. He says that the data show a positive correlation between the number of app downloads and the number of app review impressions. But he's not sure whether they would have downloaded it anyway without seeing the review. What response would be most helpful to Sam?ATrust the data. It's clear that that great review is the reason users are downloading the app.BYou could run an experiment to compare the behavior of users who didn't see the review with similar users who did. CRun an experiment to ask those who downloaded the app on their specific interests and reasons why they did itDDetermine the strength of the correlation statistics to evaluate its significance
Solution
The most helpful response to Sam would be B: "You could run an experiment to compare the behavior of users who didn't see the review with similar users who did." This would help him understand whether the review impressions are actually influencing the downloads or if people would have downloaded the app anyway. It's important to establish causation, not just correlation, in order to make informed decisions about the impact of app reviews.
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