Significance tests for a population proportion – making informed decisions about proportions

As a decision-maker, you often need to compare proportions to make informed choices. For example, you might want to know if there’s a significant difference in the proportion of satisfied customers between two product lines or if a new marketing campaign has a higher success rate than the previous one. This is where significance tests for population proportions are useful because they can allow you to compare proportions between groups and see if they are significantly different.

To provide a simple example, imagine that you wanted to run two email marketing campaigns with different content in each email and compare which was more successful. The more successful campaign could be expanded to more recipients. This is known as an A/B test and can also be a useful approach when deploying an update to a machine learning model, comparing the results between the new model and the...