Statistical significance is the probability that the result of a given study could have occurred purely by chance. It reflects the degree to which observed results are true. Hypothesis tests aim to determine if the observed difference is statistically significant.
Practical significance evaluates whether the observed difference is large enough to have any practical impact on a process under study. It evaluates the practical use of a study’s outcomes.
A hypothesis test evaluates statistical significance, whereas practical significance evaluates the significance of results considering all practical conditions. It is an inclusive decision for the process owner.
Statistical significance depends on small population differences and sample sizes, whereas practical significance looks at whether the difference is large enough to be a value in a practical sense.
Sometimes, a hypothesis test can find a claim to be statistically significant. However, a claim may not be worth the effort or expense to implement. Therefore, the organization should always consider practical significance along with statistical significance in a decision-making process. Analysts need to combine engineering judgment with statistical analysis.