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The log transformation is used to reduce skewness in distributions for better interpretation and meeting inferential statistics assumptions. Brain weight plotted against body weight shows a skewed distribution, while log transformation makes the pattern clearer. Comparing means of log-transformed data is comparing geometric means, where the antilog of the arithmetic mean equals the geometric mean. For example, the arithmetic mean of log values 0, 1, 2 is 1, while the geometric mean is the cubic root of 1,000.