<P> The qualitative interpretation of the skew is complicated and unintuitive . Skew does not refer to the direction the curve appears to be leaning; in fact, the opposite is true . For a unimodal distribution, negative skew indicates that the tail on the left side of the probability density function is longer or fatter than the right side--it does not distinguish these two kinds of shape . Conversely, positive skew indicates that the tail on the right side is longer or fatter than the left side . In cases where one tail is long but the other tail is fat, skewness does not obey a simple rule . For example, a zero value means that the tails on both sides of the mean balance out overall; this is the case for a symmetric distribution, but is also true for an asymmetric distribution where the asymmetries even out, such as one tail being long but thin, and the other being short but fat . Further, in multimodal distributions and discrete distributions, skewness is also difficult to interpret . Importantly, the skewness does not determine the relationship of mean and median . In cases where it is necessary, data might be transformed to have a normal distribution . </P> <P> Consider the two distributions in the figure just below . Within each graph, the values on the right side of the distribution taper differently from the values on the left side . These tapering sides are called tails, and they provide a visual means to determine which of the two kinds of skewness a distribution has: </P> <Ol> <Li> negative skew: The left tail is longer; the mass of the distribution is concentrated on the right of the figure . The distribution is said to be left - skewed, left - tailed, or skewed to the left, despite the fact that the curve itself appears to be skewed or leaning to the right; left instead refers to the left tail being drawn out and, often, the mean being skewed to the left of a typical center of the data . A left - skewed distribution usually appears as a right - leaning curve . </Li> <Li> positive skew: The right tail is longer; the mass of the distribution is concentrated on the left of the figure . The distribution is said to be right - skewed, right - tailed, or skewed to the right, despite the fact that the curve itself appears to be skewed or leaning to the left; right instead refers to the right tail being drawn out and, often, the mean being skewed to the right of a typical center of the data . A right - skewed distribution usually appears as a left - leaning curve . </Li> </Ol> <Li> negative skew: The left tail is longer; the mass of the distribution is concentrated on the right of the figure . The distribution is said to be left - skewed, left - tailed, or skewed to the left, despite the fact that the curve itself appears to be skewed or leaning to the right; left instead refers to the left tail being drawn out and, often, the mean being skewed to the left of a typical center of the data . A left - skewed distribution usually appears as a right - leaning curve . </Li>

What type of skew is observed in this histogram symmetry zero skew negative skew positive skew