Skewed Right
A skewed distribution with a tail that stretches right, toward the larger values.
Skewed right is a term used in statistics to describe the shape of a probability distribution. It refers to a scenario where a distribution has a tail that extends towards the right or positive side of the distribution, while the majority of the data is concentrated on the left or negative side of the distribution.
In a skewed right distribution, the mean is typically greater than the median and the mode. This is because the tail on the right side of the distribution pulls the mean in that direction. Therefore, the mean is higher than the median, which represents the value that separates the upper and lower halves of the dataset, and the mode, which is the most frequently occurring value in the dataset.
Examples of skewed right distributions include income, where a small number of individuals earn very high salaries, and exam scores, where a few high-scoring students can skew the distribution to the right. Skewed right distributions can also occur in natural phenomena, such as the distribution of lifetime of light bulbs, with many bulbs having a short life and a few having an unusually long life.
It’s important to understand the skewness of distributions as it can help determine the appropriate statistical tests and models to use. If the data is skewed, it may not be appropriate to use traditional mean-based statistics, and alternative methods such as median-based statistics may be more appropriate.
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Skewed Right Distributions In Statistics: Mean Vs. Median And Real-World Examples