given by replacing N − 1 with N − 1.5, yielding: The error in this approximation decays quadratically (as 1/N2), and it is suited for all but the smallest samples or highest precision: for N = 3 the bias is equal to 1.3%, and for N = 9 the bias is already less than 0.1%. A more accurate approximation is to replace above with . For other distributions, the correct formula depends on the distribution, but a rule of thumb is to use the further refinement of the approximation: where γ2 denotes the population excess kurtosis. The excess kurtosis may be either known beforehand for certain distributions, or estimated from the data. Confidence interval of a sampled standard deviation The standard deviation we obtain by sampling a distribution is itself not absolutely accurate, both for mathematical reasons (explained here by the confidence interval) and for practical reasons of measurement (measurement error). The mathematical effect can be described by the confidence interval or CI. To show how a larger sample will make the confidence interval narrower, consider the following examples: A small population of N = 2 has only 1 degree of freedom for estimating the standard deviation. The result is that a 95% CI of the SD runs from 0.45 × SD to 31.9 × SD; the factors here are as follows: where is the p-th quantile of the chi-square distribution with k degrees of freedom, and is the confidence level. This is equivalent to the following: With k = 1, and . The reciprocals of the square roots of these two numbers give us the factors 0.45 and 31.9 given above. A larger population of N = 10 has 9 degrees of freedom for estimating the standard deviation. The same computations as above give us in this case a 95% CI running from 0.69 × SD to 1.83 × SD. So even with a sample population of 10, the actual SD can still be almost a factor 2 higher than the sampled SD. For a sample population N=100, this is down to 0.88 × SD to 1.16 × SD. To be more certain that the sampled SD is close to the actual SD we need to sample a large number of points. These same formulae can be used to obtain confidence intervals on the variance of residuals from a least squares fit under standard normal theory, where k is