What is the disadvantage of Big O?

The disadvantages of Big O notation include:

Ignores Constants: It does not account for constant factors or lower-order terms, which can impact actual performance for smaller inputs.
Abstract Representation: It provides a high-level view without considering specific implementations or real-world conditions.
Worst-Case Focus: It often describes worst-case scenarios, which may not reflect average or best-case performance.
Complexity Classes: It categorizes algorithms by growth rates but may not highlight practical differences within the same class.
Overemphasis on Time: It primarily focuses on time complexity, potentially neglecting space complexity.
Real-World Application Challenges: Actual performance can vary due to various factors, making theoretical analysis less relevant.
Misinterpretation: Beginners may misunderstand its significance or misapply it inappropriately.