In fact in these slides from a 2011/2012 talk, Sedgewick talks about exactly this, even going as far as to draw a contrast between "TCS" and "analysis of algori

In fact in these slides from a 2011/2012 talk, Sedgewick talks about exactly this, even going as far as to draw a contrast between "TCS" and "analysis of algorithms" (the "scientific method"): https://sedgewick.io/talks/#algorithms-for-the-masses https://sedgewick.io/talks/#algorithms-for-the-masses = https://sedgewick.io/wp-content/uploads/2022/03/2011-12AlgsMasses.pdf#page=17 https://sedgewick.io/wp-content/uploads/2022/03/2011-12AlgsM... onwards. (Also touched upon briefly in https://sedgewick.io/talks/#lasting-legacy-of-flajolet https://sedgewick.io/talks/#lasting-legacy-of-flajolet = https://sedgewick.io/wp-content/uploads/2022/03/2013-14FlajoletLegacy.pdf https://sedgewick.io/wp-content/uploads/2022/03/2013-14Flajo...) His point is exactly that, often researchers care only about worst-case analysis and asymptotics, which is not necessarily a useful scientific analysis of how long an algorithm is expected to take on a machine as a function of the input size (which was the original motivation for Knuth etc to develop the field). The same authors (but ordered as Sedgewick and Flajolet!) also have a book called An Introduction to the Analysis of Algorithms; there's a course website at https://aofa.cs.princeton.edu/home/ https://aofa.cs.princeton.edu/home/ and videos on Coursera (https://www.coursera.org/learn/analysis-of-algorithms https://www.coursera.org/learn/analysis-of-algorithms) — it is advanced mathematics but less advanced than the Analytic Combinatorics book.