Correlation indicates that two metrics move together, while causation means one metric directly influences another. For example, you might notice that your app downloads increases on the same days that your website traffic spikes, showing correlation. However, this doesn't prove that higher website traffic causes more app usage; both might increase because of an external factor like a marketing campaign.

Understanding this distinction prevents costly mistakes. Imagine seeing that users who enable dark mode have 20% higher retention rates. It would be tempting to conclude that dark mode causes better retention and then heavily promote this feature. However, dark mode might simply appeal to power users who would remain engaged regardless, making the relationship merely correlational.

Testing for causation requires controlled experiments, typically A/B tests. If you randomly assign new users to either see dark mode by default (test group) or light mode (control group), and the test group shows significantly higher retention, you've demonstrated causation. Without such experiments, use caution when making decisions based on correlated metrics and consider alternative explanations for the relationships you observe.