How the FFT interprets features with frequencies below the fundamental frequency, windowing. How the FFT interprets features with frequencies above the Nyquist frequency, aliasing. How the FFT interprets features with frequencies intermediate between FFT harmonic frequencies, ‘tuning’ and padding time-series, use of dummy-data with specific frequency content. Basic approaches to replacement of missing data. Worked examples for uncomplicated data, data with intermediate frequencies, tuning datasets.