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  2. Standard deviation - Wikipedia

    One can find the standard deviation of an entire population in cases (such as standardized testing) where every member of a population is sampled.In cases where that cannot be done, the standard deviation σ is estimated by examining a random sample taken from the population and computing a statistic of the sample, which is used as an estimate of the population standard deviation.

  3. Standard error - Wikipedia

    A common source of confusion occurs when failing to distinguish clearly between the standard deviation of the population (), the standard deviation of the sample (), the standard deviation of the mean itself (¯, which is the standard error), and the estimator of the standard deviation of the mean (^ ¯, which is the most often calculated ...

  4. 68–95–99.7 rule - Wikipedia–95–99.7_rule

    The "68–95–99.7 rule" is often used to quickly get a rough probability estimate of something, given its standard deviation, if the population is assumed to be normal. It is also used as a simple test for outliers if the population is assumed normal, and as a normality test if the population is potentially not normal.

  5. Geometric standard deviation - Wikipedia

    The geometric standard deviation is used as a measure of log-normal dispersion analogously to the geometric mean. As the log-transform of a log-normal distribution results in a normal distribution, we see that the geometric standard deviation is the exponentiated value of the standard deviation of the log-transformed values, i.e.

  6. Bessel's correction - Wikipedia's_correction

    The standard deviations will then be the square roots of the respective variances. Since the square root introduces bias, the terminology "uncorrected" and "corrected" is preferred for the standard deviation estimators: s n is the uncorrected sample standard deviation (i.e. without Bessel's correction)

  7. Gaussian function - Wikipedia

    The parameter a is the height of the curve's peak, b is the position of the center of the peak, and c (the standard deviation, sometimes called the Gaussian RMS width) controls the width of the "bell".

  8. Full width at half maximum - Wikipedia

    Full width at half maximum. In a distribution, full width at half maximum ( FWHM) is the difference between the two values of the independent variable at which the dependent variable is equal to half of its maximum value. In other words, it is the width of a spectrum curve measured between those points on the y -axis which are half the maximum ...

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