> ## Content Index > Fetch the complete content index at: https://www.accelerateux.com/llms.txt > Use this file to discover other available public pages before exploring further. # Correlation vs. Causation - URL: https://www.accelerateux.com/resources/analytics/correlation-vs-causation/ - Published: 2026-09-16T22:00:26.000Z - Updated: 2026-09-16T22:00:25.000Z - Description: Build a critical-thinking skill for reading analytics—avoid drawing false conclusions from data that shifts at the same time in unrelated ways. - Author: Molly Gertenbach - Tags: Resource Item, Analytics, Getting Started ## What correlation and causation actually mean Causation is when a change in one data point directly causes a change in another data point. Correlation, on the other hand, is when two data points change at the same time—but not necessarily from any impact on each other. Analytics tools make it incredibly easy to spot instances of correlation but it's important not to jump to immediate assumptions of causation. ## Why the distinction of correlation vs. causation matters Making changes based on assumed causation can lead to wasted time and money; ultimately leading you to reverse changes that were never actually a problem. We love to use the following example to illustrate how easy it is to incorrectly represent causation when there isn't any. *Did you know...* when ice cream sales increase, there is a correlated increase in shark attacks? When presenting isolated (but correlated) data points such as these you should avoid jumping to what sounds like an obvious conclusion that eating/selling ice cream is directly causing shark attacks. In reality, there's no causation between these two clearly correlated data points—banning ice cream would have zero impact on reducing shark attacks. Rather, this correlation occurs because both events (eating ice cream and getting attacked by a shark) are impacted by a shared factor: *Warm weather* causes people to eat more ice cream and spend more time in the ocean—leading to more shark attacks to be possible. Since small businesses often have limited time to test changes and lower budgets for making improvements, the risk of chasing false causation is much higher. ## A real life example The shark attack vs. ice cream example is a little funny, and an easy way to illustrate the distinction between correlation and causation in data points. Within your website analytics these distinctions can be less obvious; it's important to actively remind yourself to investigate the causation behind correlation before taking action. For example, if you notice conversions dip the same week a new product was launched you might want to assume that the new product hurt your conversion rate. While this *could* be the case, it's highly likely that something else—a seasonal dip, broken links, a change in algorithms impacting promotion—is causing the impact; timing may just be a coincidence. Jumping to the first conclusion that correlation in data sets might point to could cause you to make a hasty decision to pull the product; in reality it had nothing to do with the performance shift. ## Common mistakes to avoid making As you review data and spot areas of correlation watch out for the following mistakes that are easy to make: - Don't assume a recent website change is automatically responsible for a shift in metrics without first ruling out other factors such as seasonal trends, external events, and shifts in traffic sources. - Don't confuse one data point changing right after an event or other change in data with that data point changing *because* of an event or other change in data. - Don't overlook when multiple changes have occurred simultaneously—such as launching a redesign at the same time as a new ad campaign. Both of these events could be impacting performance in separate ways, but the overlap makes it impossible to distinguish how they are doing so. ## Reducing the risk of wrong conclusions ### Check for shifts in unrelated metrics Before jumping to conclusions, review other unrelated metrics to see if they shifted at the same time. For example—if conversions drop right after a redesign, but you've also lost a significant volume of traffic from social media the issue may have a broader, unrelated cause. ### Avoid making multiple changes at the same time Try to isolate changes and updates to your website to launch one at a time. This will help you better evaluate the impact related to any single update more reliably. ### Use A/B testing when possible Test in changes as much as possible to accurately identify the impact and cause of performance shifts instead of relying on assumptions. ## How to investigate suspicious correlations in data ### Compare across traffic sources and segments See how the impact shifts when viewed agains other traffic sources and segments. Is it consistently impacted, or is it isolated to a single channel—indicating the possibility of an issue related to that audience. ### Compare time periods See how performance shifts across a broader window of time, and compare performance against similar time windows in previous months and years. Watch for recurring or similar fluctuations that may indicate seasonal behavior patterns. ### Review other changes As yourself what other changes happened at the same time *before* making a final conclusion about cause.