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1238 Discussion Papers Deutsche Institute for Wirtschaftsforschung Hidden Skewness Ludwig Esther, Olga Notetaker and Georg Wacker 2012 Opinions expressed in this paper are those of the author(s) and
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Start by identifying the dataset or distribution for which you suspect hidden skewness. This could be a set of numbers, a histogram, or any other representation of data.
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Calculate the skewness of the dataset using an appropriate statistical formula or software. Skewness refers to the measure of asymmetry in the distribution of data. Positive skewness indicates a longer right tail, while negative skewness indicates a longer left tail.
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If the skewness value is significant (greater than a certain threshold or deemed problematic for your analysis), you can proceed to address the hidden skewness.
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Explore various transformation options to reduce or eliminate the skewness. Common transformation methods include logarithmic, square root, or Box-Cox transformations. Choose the transformation that produces the most symmetrical distribution, as assessed through visual inspections or statistical tests.
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Apply the selected transformation method to the dataset. This can typically be done using mathematical formulas or built-in functions in statistical software tools.
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Researchers or statisticians who are analyzing datasets and need to ensure the data meet the assumption of normality or symmetry.
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Anyone working with skewed data who wants to explore the impact of transforming the data to achieve better results in their analysis or modeling.
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