Now, I have to wonder: (1) In political polling, could he give a solid definition of a simple random sample? Could he suggest how to select one? Could he explain just why we often want a simple random sample? (2) Could he explain why in political polling we average to estimate a population proportion? That is, the average is a statistical estimator. Just why is it the estimator we want? (3) If he is to estimate variance, does he know which of the two common algebraic expressions is biased and which is unbiased? Can he define a biased estimator? (4) A common topic in political science is principal components analysis, and key there is orthogonality in vector spaces. Can he suggestion how to give a solid description of that topic without algebra? (5) Political science likes to construct quantitative measures, and there two important issues are 'reliability' and 'validity'. Can he give solid discussions of these two issues without algebra? (6) Political polling reports results with an 'margin of error'. Could he define that? The usual calculation is based on the central limit theorem; could he describe that? (7) One of the more important research tools in political science is statistical hypothesis testing, especially nonparametrics. There the core concepts are Type I error, Type II error, power of a test, and a most powerful test. How can he describe this material without algebra?
Uh, a first course in algorithms commonly described heap sort and points out that its execution time, on sorting n items, is O( n log(n) ) in both worst case and average case and, thus, is best possible in that it meets the Gleason bound. Without algebra, how is a student to understand the significance of that material?
The world is moving on to more in automation. The main approach here is computing where we describe what we want and how to get it in terms that are essentially mathematical -- e.g., stiffness of arbitrary space frames, finite element analysis in solid mechanics, Reynolds number in fluid flow, sines, cosines, and exponentials in A/C circuit analysis with passive components, force, torque, energy, power, and momentum in mechanical engineering, etc. All these subjects commonly assume first and second year algebra, plane and solid geometry, trigonometry, analytic geometry, calculus, and ordinary differential equations.
For political science, we should include linear algebra and multivariate statistics.
That author wants less algebra but more statistics? That's like saying he wants to lose weight but eat more!
Yes, to too great an extent, public K-12 is more about babysitting, i.e., keeping the kids off the streets, than about education. Moreover, really, math is too difficult for essentially all of the US K-12 educational community.