Practical Significance

Practical significance evaluates whether an experiment result matters for the business, regardless of its statistical significance. A result can be statistically significant (unlikely to be due to chance) but practically insignificant (too small to be worth acting on), or statistically insignificant (the experiment was underpowered) but potentially practically significant (the point estimate suggests a meaningful effect). For growth teams, practical significance is the decision-relevant criterion because shipping a change has real costs: engineering effort to implement and maintain, codebase complexity, user experience disruption, and opportunity cost of not pursuing other changes. A statistically significant 0.02% improvement in click-through rate is real, but if implementing it requires a week of engineering time, it is not worth shipping.

Determining practical significance requires pre-specifying the minimum effect size of interest (MESOI) before the experiment. The MESOI should reflect the business context: the cost of implementation, the revenue impact at scale, the strategic importance of the metric, and the opportunity cost of engineering resources. For a large-scale product with millions of daily users, a 0.1% conversion improvement might generate millions in annual revenue and be highly practically significant. For a small feature used by thousands of users, a 5% improvement might be necessary to justify the investment. The analysis should compare the confidence interval to the MESOI: if the entire CI is above the MESOI, the result is both statistically and practically significant. If the CI includes the MESOI but also extends below it, the result is ambiguous. If the CI is entirely below the MESOI (even if above zero), the result is statistically significant but not practically significant.

Teams should establish practical significance thresholds as part of their experiment planning process and document them in the analysis plan. This prevents the common failure mode of post-hoc rationalization, where any statistically significant result is declared a win regardless of its magnitude. Common pitfalls include not setting practical significance criteria before the experiment, confusing statistical significance with business impact, celebrating small relative effects that sound impressive but have trivial absolute impact (a 50% lift from 0.01% to 0.015%), and ignoring the confidence interval width when making ship decisions. Equivalence testing provides a formal framework for concluding that a treatment is practically equivalent to the control: if the entire CI falls within the MESOI bounds around zero, the treatment can be declared non-inferior.

Advanced practical significance frameworks include decision-theoretic approaches that formally model the costs and benefits of shipping versus not shipping, incorporating the full posterior distribution of the treatment effect and the cost structure of implementation. Expected value of information (EVOI) calculations determine whether running an experiment is worthwhile in the first place, given the prior uncertainty about the effect size and the cost of the experiment. For organizations running many experiments, practical significance thresholds can be calibrated against the historical distribution of effect sizes to ensure they are realistic. Some teams use a lift-effort matrix that plots the estimated effect size against the implementation effort to prioritize which winning experiments to ship, recognizing that not all statistically and practically significant results deserve equal priority.