Success Stories of "Statistics"? When it comes to Machine Learning, the successful application seem to be very well known. For example, Neural Networks have been successfully used to create Self Driving Cars and Game Playing Algorithms (e.g. Go, Chess).
When we think of classical statistical models such as regression model, it seems more difficult to think of equally well known and successful applications of such models.
I understand that this might be in part because Machine Learning might get more news/media attention and the problems in which Machine Learning tends to be applied in are in some ways easier to quantify success - e.g. does this neural network beat the world champion?. I also understand that Machine Learning itself is heavily rooted in statistics, e.g. PAC learnability.
But in terms of the classical statistical models such as regression, have their been any successful applications of these models on a similar scale to the successful applications of machine learning?
Thanks!

The German Tank Problem is a statistical approach to estimating a population size given a sample. The goal is to estimate the total number of items $N$, given a random sample of the population which has observable serial numbers from $1$ to $N$.
The problem is so named for its real-life application, in which Allied intelligence agencies wanted to estimate the number of German tanks being produced during World War II. By observing the serial numbers on a limited number of destroyed tanks, statisticians were able to infer the total number of tanks in the population with remarkable accuracy. Post-war analysis revealed that the statistical estimates were often superior to the estimates generated from conventional intelligence methods.
Other statistics developed during wartime include the Receiver Operator Characteristic (ROC) curve, which is a means of evaluating a classifier. WWII radar operators would have to classify radar blips as either enemy planes, or false alarms like birds or weather. The development of the ROC curve allowed a principled means of evaluating the performance of an individual radar operator, indicating whether they could correctly and reliably identify enemy aircraft, or if they would require further training. The ROC curve is used in many fields from medicine to meteorology to evaluate the performance of a classification method.
