Statistical Significance vs Practical Significance: What's the Difference?

Statistical significance is the confidence that a measured difference between a test variant and the control reflects a real effect rather than random chance - typically expressed through a p-value, with a result below a preset threshold (commonly 0.05) considered unlikely to be noise.

Practical significance is whether that difference, even once confirmed as real, is actually large enough to matter for the business - translating into revenue, conversions, or another outcome worth the cost and effort of implementing the change.

The two can diverge in a way that trips up test interpretation: with a large enough sample size, even a genuinely tiny, commercially trivial improvement can become statistically significant, since statistical tests get more sensitive to smaller differences as sample size grows. A result can clear the statistical bar easily and still not be worth acting on if the actual size of the lift is negligible in practical terms. Conversely, a change with real practical significance can fail to reach statistical significance if the test simply didn't run long enough or with enough traffic to confirm the effect wasn't chance. Reading both together - confirming a result is real, and then separately judging whether it's big enough to matter - is what keeps a testing program from either implementing noise as if it were a genuine win, or dismissing a real, valuable effect because the test lacked the power to prove it.