I started college in 1982. At that time, calculators were common, but not computers. The data sets had to be small enough for us to work problems by hand. Not any more. I see no reason why a stats course can't start out with big bright data sets that are easy to analyze, then advance through more difficult problems where it becomes progressively easier to get things wrong, and thus requires more sophistication to think about problems.
I just want to add a bit more. It's quite easy today, to generate and play with random numbers. If you think you understand a process that has generated your data simulate it and run the simulated data through the same analysis. I do this for real -- I don't trust myself to choose the right statistical analysis, so I always test my chosen analysis with simulated data. If I can fool myself with simulated data, than my real data is probably fooling me too.
Could we, for instance, collect enough data on typing discipline to end the static/dynamic typing once and for all? Enough data to overcome the priors of both static typing and dynamic typing proponents?
We could, but that would require pretty big sample sizes. Like 10,000 developers of various competence, working on 1,000 projects of various domains and difficulties for various amounts of time (from a few days to at least a few months). Who is ever going to fund that?
Until we get such a miracle controlled study, our respective priors will still matter.
I just want to add a bit more. It's quite easy today, to generate and play with random numbers. If you think you understand a process that has generated your data simulate it and run the simulated data through the same analysis. I do this for real -- I don't trust myself to choose the right statistical analysis, so I always test my chosen analysis with simulated data. If I can fool myself with simulated data, than my real data is probably fooling me too.