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Original
2026-01-01
Modified
2026-02-28
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<p>Skewness indicates how data points are spread in a dataset. We can classify skewness into two main types:</p>
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<p>Skewness indicates how data points are spread in a dataset. We can classify skewness into two main types:</p>
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<p><strong>Positive Skewness:</strong> In a positively skewed distribution, the mean will be<a>greater than</a>the median, which is greater than the mode. This implies that the distribution has a longer tail towards the right side, where the extreme values pull the mean towards the right.</p>
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<p><strong>Positive Skewness:</strong> In a positively skewed distribution, the mean will be<a>greater than</a>the median, which is greater than the mode. This implies that the distribution has a longer tail towards the right side, where the extreme values pull the mean towards the right.</p>
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<p><strong>Negative Skewness:</strong> In a negative skewed distribution, the mean will be<a>less than</a>the<a>median</a>, which is less than the mode. This means that the distribution has a longer tail towards the left side, with a few extreme values pulling the mean towards the left. There are several measures that we use to quantify the skewness in a distribution. Some of the most commonly used measures are:</p>
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<p><strong>Negative Skewness:</strong> In a negative skewed distribution, the mean will be<a>less than</a>the<a>median</a>, which is less than the mode. This means that the distribution has a longer tail towards the left side, with a few extreme values pulling the mean towards the left. There are several measures that we use to quantify the skewness in a distribution. Some of the most commonly used measures are:</p>
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<p><strong>Pearson’s First Coefficient:</strong> Pearson’s First Coefficient, also known as the moment coefficient of skewness, measures the skewness of a distribution. It is a measure of skewness used to compare the mean and mode of a data distribution. It determines the direction and the extent of the skewness in the data. The<a>formula</a>we use for Pearson’s first coefficient is: Pearson’s first coefficient formula = (Mean - Mode) / Standard Deviation</p>
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<p><strong>Pearson’s First Coefficient:</strong> Pearson’s First Coefficient, also known as the moment coefficient of skewness, measures the skewness of a distribution. It is a measure of skewness used to compare the mean and mode of a data distribution. It determines the direction and the extent of the skewness in the data. The<a>formula</a>we use for Pearson’s first coefficient is: Pearson’s first coefficient formula = (Mean - Mode) / Standard Deviation</p>
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<p>Where:</p>
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<p>Where:</p>
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<p>Mean is the<a>average</a>of the values in the dataset</p>
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<p>Mean is the<a>average</a>of the values in the dataset</p>
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<p>Mode is the most frequently occurring value in the dataset</p>
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<p>Mode is the most frequently occurring value in the dataset</p>
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<p>Standard Deviation is a measure of the amount of variation in the dataset.</p>
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<p>Standard Deviation is a measure of the amount of variation in the dataset.</p>
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<p>If mean > mode, the skewness is positive (right-skewed)</p>
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<p>If mean > mode, the skewness is positive (right-skewed)</p>
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<p>If mean < mode, the skewness is negative (left-skewed)</p>
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<p>If mean < mode, the skewness is negative (left-skewed)</p>
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<p>If mean ≈ mode, the skewness is symmetric</p>
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<p>If mean ≈ mode, the skewness is symmetric</p>
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<p><strong>Pearson’s Second Coefficient of Skewness: </strong>Compared to Pearson’s first coefficient, it is less influenced by outliers or any extreme values in the distributions. We use Pearson’s second coefficient if the mode is not well-defined. The formula we use is:</p>
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<p><strong>Pearson’s Second Coefficient of Skewness: </strong>Compared to Pearson’s first coefficient, it is less influenced by outliers or any extreme values in the distributions. We use Pearson’s second coefficient if the mode is not well-defined. The formula we use is:</p>
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<p>Pearson’s Second Coefficient Formula = 3 × (Mean - Median) / Standard Deviation</p>
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<p>Pearson’s Second Coefficient Formula = 3 × (Mean - Median) / Standard Deviation</p>
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<p>Where:</p>
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<p>Where:</p>
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<p>Mean is the average of the values in the dataset</p>
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<p>Mean is the average of the values in the dataset</p>
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<p>Median is the central value in the dataset</p>
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<p>Median is the central value in the dataset</p>
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<p>Standard Deviation is a measure of the amount of variation in the dataset.</p>
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<p>Standard Deviation is a measure of the amount of variation in the dataset.</p>
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<p>If mean > median, the skewness is positive (right-skewed)</p>
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<p>If mean > median, the skewness is positive (right-skewed)</p>
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<p>If mean < median, the skewness is negative (left-skewed)</p>
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<p>If mean < median, the skewness is negative (left-skewed)</p>
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<p>If mean ≈ median, the skewness is symmetric</p>
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<p>If mean ≈ median, the skewness is symmetric</p>
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<p>These are the two formulas used to calculate Pearson’s coefficient of skewness.</p>
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<p>These are the two formulas used to calculate Pearson’s coefficient of skewness.</p>
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