Confirmatory Data Analysis is the part where you evaluate your evidence using traditional statistical tools such as significance, inference, and confidence.
At this point, you’re really challenging your assumptions. A big part of confirmatory data analysis is quantifying things like the extent any deviation from the model you’ve built could have happened by chance, and at what point you need to start questioning your model.
Confirmatory Data Analysis involves things like: testing hypotheses, producing estimates with a specified level of precision, regression analysis, and variance analysis. In this way, your confirmatory data analysis is where you put your findings and arguments to trial.
In reality, exploratory and confirmatory data analysis aren’t performed one after another, but continually intertwine to help you create the best possible model for analysis.
Let’s take an example of how this might look in practice.
Imagine that in recent months, you’d seen a surge in the number of users canceling their product subscription. You want to find out why this is, so that you can tackle the underlying cause and reverse the trend.