1991Drug Development and Industrial PharmacyRequires access

Determination of Geometric Standard Deviation for Dissolution

N. R. Bohidar

Open publisher page 6 citations

Abstract

The two important instances in which the scientist converts his/her experimental data to a logarithmic scale prior to computing the mean and standard deviation, are (i) when the distribution of the data is asymmetrical (e.g. percentage data) and (ii) when he/she intends to compare statistically the averages of two or more groups with unequal standard deviations. In either case, the mean is restored to its original scale by taking the antilog of the log mean, which is the geometric mean. However, this procedure cannot be applied for computing the geometric standard deviation. The author of reference(l) erroneously claims that the antilog of log standard deviation is the geometric standard deviation. This paper demonstrates the incorrectness of the procedure in reference (1), exhibits the exact statistical formula and introduces a novel method called “jackknife statistic” to confirm the results, based on the dissolution data associated with Product-C.

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What this paper is about

The two important instances in which the scientist converts his/her experimental data to a logarithmic scale prior to computing the mean and standard deviation, are (i) when the distribution of the data is asymmetrical (e.g. percentage data) and (ii) when he/she intends to compare statistically the averages of two or more groups with unequal standard deviations. In either case, the mean is restored to its original scale by taking the antilog of the log mean, which is the geometric mean. However, this procedure cannot be applied for computing the geometric standard deviation. The author of reference(l) erroneously claims that the antilog of log standard deviation is the geometric standard deviation. This paper demonstrates the incorrectness of the procedure in reference (1), exhibits the exact statistical formula and introduces a novel method called “jackknife statistic” to confirm the results, based on the dissolution data associated with Product-C.

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Available abstract

The two important instances in which the scientist converts his/her experimental data to a logarithmic scale prior to computing the mean and standard deviation, are (i) when the distribution of the data is asymmetrical (e.g. percentage data) and (ii) when he/she intends to compare statistically the averages of two or more groups with unequal standard deviations. In either case, the mean is restored to its original scale by taking the antilog of the log mean, which is the geometric mean. However, this procedure cannot be applied for computing the geometric standard deviation. The author of reference(l) erroneously claims that the antilog of log standard deviation is the geometric standard deviation. This paper demonstrates the incorrectness of the procedure in reference (1), exhibits the exact statistical formula and introduces a novel method called “jackknife statistic” to confirm the results, based on the dissolution data associated with Product-C.

Key concepts: Geometric standard deviation, Standard deviation, Geometric mean, Studentized range, Mathematics, Jackknife resampling, Relative standard deviation, Statistics

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