While you're correct that condition number is a more robust numerical method for arbitrary matrices, the determinant is certainly suitable for many matrices. Fo

While you're correct that condition number is a more robust numerical method for arbitrary matrices, the determinant is certainly suitable for many matrices. For small matrices with small integer values such as this one, there is no issue with the determinant. There is no one bulletproof general method to approximate mathematical calculations with floating point numbers. More context is generally required, including the actual problem that is being approximated, to determine if a method is reliable. Painting this as a black and white situation where the determinant is wrong and the condition number is right gives a misleading picture of how we evaluate numerical methods for fit-to-purpose.