Why might a statistically significant result not always be practically significant?

Statistical significance indicates that an observed effect in data is unlikely to have occurred due to random chance alone, typically measured using a p-value threshold such as p < 0.05. However, this does not necessarily mean the effect is large, important, or actionable in a real-world context. With very large sample sizes, even minuscule differences between treatment and control groups can produce statistically significant results. For example, a 0.2% increase in click-through rate might be statistically significant but yield negligible revenue impact.

Practical significance considers the real-world impact of a result: Is the change noticeable to users? Does it meaningfully affect business goals, customer experience, or operational efficiency? Teams should always evaluate the effect size, cost of implementation, and potential trade-offs before making decisions based solely on statistical significance. Ultimately, a decision backed by significant data should also pass a common-sense threshold of relevance and value.