Confirmation bias is our tendency to focus on data that supports our existing beliefs while overlooking contradictory information.^[1] In metrics analysis, this manifests when we eagerly embrace numbers that validate our hypotheses but dismiss or explain away metrics that challenge them. This bias can derail product decisions and lead to missed opportunities.

Countering confirmation bias requires deliberately seeking evidence that contradicts your assumptions. Start by formulating a null hypothesis (H₀) that states "there is no relationship" or "no effect" between variables, alongside an alternative hypothesis (H₁) representing what you're actually trying to prove. The goal then is to rigorously test whether there's sufficient evidence to reject the null hypothesis.

Structured analysis techniques also help minimize bias:

• Segment your data in different ways to see if patterns hold across various user groups.
• Set specific thresholds for metrics in advance to prevent moving goalposts later.
• Consider asking colleagues who weren't involved in the initial hypothesis to review your findings with fresh eyes.

Remember that failing to reject the null hypothesis isn't a failure. It provides valuable learning that prevents wasted resources on false assumptions.