The Empirical Rule: A Powerful Tool For Analyzing Normal Distributions In Finance And Beyond.

Skewed Left

A skewed distribution with a tail that stretches left, toward the smaller values.

In statistics, a skewed left distribution, also known as a negative skew or left-tailed distribution, is a type of probability distribution where most of the data points are concentrated toward the right-hand side of the chart, with a longer tail stretching toward the left-hand side.

Characteristics of a skewed left distribution:

1. Mean < Median: The mean value is typically less than the median, with the median being the point where 50% of the observations fall below and 50% fall above. 2. Mode < Median < Mean: The mode is less than the median and the median is less than the mean. 3. Long Left Tail: The long tail stretches in the negative direction, with several outliers in the left-hand side of the chart. 4. Fewer Data Points towards the Left: There are fewer data points in the left tail of the distribution, compared to the right-hand side where the data points are heavily concentrated. Examples of skewed left distributions include household income, age distribution in a retirement community, or time required to solve a particular problem. It is important to identify the type of distribution in statistical analysis, as skewed left distributions may impact statistical inferences, and strategies for normalization of the data might be required to perform statistical tests.

More Answers: