A plot of relative deviation from the mean and its frequency of occurrence appears as a Gaussian curve. This probability distribution curve of a population can be described by the population mean and standard deviation. The standard deviation measures the spread of clustered data about the mean. When the standard deviation is lower, the distribution about the mean is narrower, leading to high precision. For a finite number of measurements – the population subset – the mean and standard deviation are denoted as the sample mean and sample standard deviation, respectively. A set of N measurements can have N deviations from a reference point – true mean, which means N degrees of freedom. However, if the estimated mean is set as a reference point, only N-1 independent deviations are required, as the last one is predetermined. The standard deviation expressed as a fraction of the mean is called the relative standard deviation. When given as a percentage, it is the coefficient of variation. Variance is the square of standard deviation.