A Misconception About Log Transform
...and here's what it does.
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Misconception About Log Transform
Log transform is commonly used to eliminate skewness in data.
Yet, it is not always the ideal solution for eliminating skewness.
It is important to note that log transform:
DOES NOT eliminate left-skewness.
Only works for right-skewness, that too when the values are small and positive.
To understand better, consider the left-skewed distribution and its log transform below:
It is clear that log transform did not eliminate skewness.
However, now consider a right-skewed distribution:
This time, it eliminates the skewness.
This happens because the log function grows faster for lower values. Thus, it stretches out the lower values more than the higher values.
More specifically, in the case of left-skewness, the tail exists to the left, which gets stretched more than the majority of the probability mass that exists to the right. Thus, skewness isn't affected much.
However, in the case of right-skewness, the majority of probability mass exists to the left, which gets stretched out more.
That said, it is important to note that even if we have a right-skewed distribution, log transform will not be effective if the values are large.
This happens because the stretch effect of the log function diminishes at large values:
I have often found the box-cox transform to be quite effective at eliminating both right-skewness and left-skewness, as depicted below:
To read next, here are 8 Fatal (Yet Non-obvious) Pitfalls and Cautionary Measures in Data Science.
👉 Over to you: What are some other ways to eliminate skewness?
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