Which distribution type is indicated when the direction of the tail affects the shape and is labeled as positively or negatively skewed?

A skewed distribution is characterized by the asymmetrical nature of its probability distribution, where one tail is longer or fatter than the other. When a distribution is labeled as positively skewed, it means that the tail on the right side is longer or fatter, indicating that there are a significant number of high-value outliers. Conversely, a negatively skewed distribution has a longer or fatter tail on the left side, which suggests a prevalence of low-value outliers.

This distinction in skewness has important implications for statistical analysis and interpretation, as it affects measures of central tendency. In positively skewed distributions, the mean is typically greater than the median, while in negatively skewed distributions, the mean is less than the median. Recognizing the type of skewness is crucial for selecting appropriate statistical tests and for understanding the underlying data better.

The other distribution types mentioned do not exhibit this characteristic of skewness. A bimodal distribution features two modes or peaks, which is fundamentally different from the concept of skewness. A normal distribution is symmetrical with no skew, showing equal tails. An exponential distribution typically models the time until an event occurs and does not involve skewness in the same context as discussed here. Thus, the choice of skewed distribution