A crash course in statistics

I recently managed to explain most of statistics (no, seriously) in about an afternoon using the first chapter of Intermediate Statistics: A Conceptual Course. (Sage, Pelham, B. W. (2012)) and three graphs*. After  many years of working with statistics, I actually believe it should be that easy. Also, at work, I have seen total novices use stats professionally in a matter of weeks. It is simply not that hard!
Of course, I'm not talking about assumptions, time series, endogeneity, robust standard errors, and everything that gets your stats right. I refer to what's needed to understand and report test that will be close enough. After all, a complex GMM regression with bootstrapped standard errors still return a regression coefficient and significance levels.
This would be the content of such a course:

1. Samples and inferential statistics
2. Average and variance
3. The normal distribution (probability, significance)
4. Chi2 (test and distribution)
5. Correlations and odds ratios
6. Regression (linear and nonlinear effects)
7. Factor analysis
8. Cluster analysis (similarity)

* One with linear and quadratic relations (for correlations and multiple regression), one with a factor score from two dimensions (also based on correlation), and one with clusters on a plot (similarity).