Which transformation is commonly used to reduce right-skewed distributions?

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BlackTom AI · Accepted Answer

The question asks about a transformation commonly used to reduce right-skewed distributions.
Option presented: a. Log transformation.
First, consider what a right-skewed (positively skewed) distribution looks like: a long tail extends to the right, with a cluster of smaller values on the left. Applying a log transformation compresses the high end of the scale more than the low end, which tends to pull in the long tail and make the distribution more symmetric.
Next, think about variance and modeling implications. Log transforming data can stabilize variance when data are multiplicative or follow a log-normal pattern, making methods that assume normality or homoscedasticity more appropriate.
Additionally, the transformation is particularly effective for data that are strictly positive, as the logarithm is undefined for zero or negative values; in practice, small positive data or shifted data may be used to meet this requirement.
Now, regarding potential alternatives (even though none are listed): other common transformations like square root or Box-Cox can also address skewness under certain conditions, but the log transformation is the classic and frequently recommended choice for right skew, especially with positively skewed financial, biological, or count data.
In summary, the log transformation is well-known for reducing right skew by compressing large values, stabilizing variance, and aligning the data more closely with normal-theory assumptions when appropriate.

COMM5000-Data Literacy for Business - T1/2025

Which transformation is commonly used to reduce right-skewed distributions?

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