Hypothesis testing is a statistical method for assessing whether sample data provide sufficient evidence to reject a stated assumption about a population or process.
In DMAIC projects, teams use hypothesis tests to investigate suspected causes and evaluate changes in performance. The null hypothesis usually represents no difference or no association. Before analysing the data, the team defines the comparison and selects a significance level, commonly 0.05. A p-value below that threshold supports rejecting the null hypothesis under the test’s assumptions. It does not measure the probability that the null hypothesis is true.
For example, an accounts-payable team might test whether average invoice-processing time changed after introducing a revised approval workflow. The analysis must account for differences in invoice complexity and whether observations are independent. Comparing unmatched periods with different workloads can produce a misleading conclusion. A confidence interval helps the reviewer assess whether the estimated reduction is large enough to justify implementation.
Hypothesis testing fits within the Analyze and Improve phases of DMAIC. Measurement-system validation establishes whether the underlying data are suitable, while design of experiments can structure comparisons that investigate causal effects. Statistical significance alone does not establish a root cause or a financial benefit. An invoice-processing project still needs evidence connecting the workflow change to the observed reduction, with released staff capacity accounted for separately from reduced expenditure.
