Floating point error occurs when computers represent real numbers using a finite number of binary digits. This limitation can lead to inaccuracies in calculations, especially when dealing with very large or very small numbers. For example, the number 0.1 cannot be precisely represented in binary, resulting in small discrepancies during arithmetic operations. These errors can accumulate over time, particularly in iterative calculations or complex algorithms. As a result, the final output may differ from the expected value. Understanding floating point error is crucial for programmers and engineers, especially when working with scientific computations or financial applications where precision is essential.