Statistical significance helps determine whether observed data patterns represent real trends or just random variations. Understanding this concept is crucial for avoiding false conclusions when analyzing product metrics. You don't need to be a statistician, but grasping the fundamentals will strengthen your data confidence.

Key concepts to understand include:

• Statistical significance: Indicates that an observed effect is unlikely to have occurred by random chance. Typically, results are considered significant when there's less than a 5% probability (p < 0.05) they occurred randomly. Some teams may use a 10% probability when working with smaller sample sizes and faster timelines.
• Confidence level: The probability that your findings accurately reflect the true pattern in the overall population. Common confidence levels are 90% or 95%, meaning you can be 90% or 95% confident your results represent reality rather than coincidence.
• Sample size: While larger samples do give more reliable results, they also take longer to collect. That’s a tradeoff: the bigger your sample, the slower your test. In many cases, running several smaller, faster tests can give you quicker insights than waiting for one large test to finish.