skewness

the skewness of the income distribution is strongly positive in this dataset.

we report skewness and kurtosis to describe the shape of the data.

the sample skewness suggests a long right tail in the measurements.

after a log transform, the skewness drops close to zero.

negative skewness indicates the distribution is left skewed.

the histogram confirms the skewness we saw in the summary statistics.

high skewness can distort the mean and inflate the standard deviation.

we check skewness before fitting a normal model.

the residuals show slight skewness, so we use robust standard errors.

bootstrapping helps when skewness makes the sampling distribution nonnormal.

outliers often increase skewness, so we inspect extreme values carefully.

she compares skewness across groups to see which subgroup has a heavier tail.