The main difference, and reason why your analogy isn't entirely accurate, is that while with games you can see when things aren't working, when using ML or stats you will _always_ get a number. Whether or not that number is meaningful requires some amount of domain knowledge a lot of the time. I have a degree in stats and someone at work who does not was trying to use these frameworks to analyse log files. When I had a look at it, his results were showing that they were statistically significant, but the data didn't look anything like a linear relationship and fitting it to a regression wasn't a valid move. That's a simplistic example but even in the relatively simple realm of linear regression there are more difficult traps to spot, like heterostedasticity or error normality.
I agree that one shouldn't apply ML in a commercial context without understanding it. But I think that's true about almost anything. I can't think of a technology I use for which I don't have a corresponding "novices did it all wrong" story.
But here we're talking about a series of intro videos and the appropriate pedagogical approach. It really could be that ML has more subtle failure modes than programming, although I'm suspicious; I remember a lot of my novice C issues where the program did happen to appear to work, at least for short periods, even though my code was terrible. But if it is, I think the trick there isn't to prescribe a heavier dose of theory, it's to get people to experience problems like you describe in a way where they can quickly detect and learn from them.
