In the context of business analytics, how can a standard curve generation help assess the statistical significance of a new marketing strategy's impact on sales metrics?

In business analytics, generating a standard curve lets you model the expected sales distribution under normal conditions, creating a baseline of what sales normally look like without the new strategy. By plotting the actual sales after the strategy against this curve, you can see how far the new results deviate from the usual pattern. If the observed sales lie far outside the curve’s confidence bounds, the change is unlikely due to chance and is statistically significant. For example, if the standard curve predicts sales of 100 units with a 95% confidence interval of 90–110 units, but the strategy yields 130 units, the difference suggests a real impact. Thus, the curve provides a visual and quantitative test of whether the marketing effort truly altered sales.