The main purpose of algorithm analysis is to evaluate its efficiency and scalability. By determining an algorithm's time and space complexity (e.g., using Big O notation), we can estimate how its performance will degrade as the input size grows. This theoretical analysis is crucial for selecting an algorithm that will remain performant and feasible when deployed on large-scale datasets, thereby avoiding costly performance bottlenecks that might only appear after implementation and deployment.

B. Algorithm analysis is used to estimate memory usage (space complexity), not to eliminate it entirely.

C. Analysis is a theoretical tool for performance prediction; it does not replace the need for testing to ensure correctness.

D. Algorithm analysis is concerned with computational efficiency, which is unrelated to user interface aesthetics.

- Sedgewick, R., & Wayne, K. (2011). Algorithms (4th ed.). Addison-Wesley Professional. Section 1.4, "Analysis of Algorithms," establishes the framework for predicting performance and comparing algorithms.

- MIT OpenCourseWare. (2011). 6.006 Introduction to Algorithms, Fall 2011. Lecture 1: Algorithmic Thinking, Peak Finding. The lecture introduces the core motivation for algorithm analysis as a way to reason about scalability.