In the context of signal analysis, it turns out that the Discrete Fourier Transform} (DFT) is the tool that finds out the frequency domain amplitudes and phases of $N$ complex sinusoids present in a discrete-time signal of length $N$ samples. Just like DFT is used to convert a signal from time to frequency domain, Inverse Discrete Fourier Transform (iDFT) is used as an inverse process by converting a signal from frequency to time domain. A natural question at this stage is: how well does the DFT — a sampled frequency function of a sampled time domain signal — approximates the underlying Fourier transform — a continuous frequency function of a continuous time domain signal?