Understanding Skewed Right Distributions | Exploring the Asymmetry in Probability Distributions

skewed right

In statistics, a skewness refers to the measure of the asymmetry of a probability distribution

In statistics, a skewness refers to the measure of the asymmetry of a probability distribution. Skewness can be categorized into three types: symmetric, positively skewed (skewed right), and negatively skewed (skewed left).

When a distribution is skewed right (positively skewed), it means that the tail of the distribution extends towards the right side, while the majority of the data is concentrated towards the left side. In other words, the distribution has a long tail on the right side.

In a positively skewed distribution, the mean is usually greater than the median. This is because the presence of the longer tail on the right side pulls the mean towards higher values. The median, on the other hand, is less affected by extreme values and tends to be a better measure of central tendency in skewed distributions.

Visualizing a skewed right distribution, you can imagine a histogram where the bars are higher on the left side and gradually decrease as you move towards the right side. The distribution may resemble a “leaning” or “tilted” shape towards the right.

It’s worth noting that while skewness describes the shape of the distribution, it does not indicate whether the data is good or bad. Skewed right distributions are common in various fields, such as finance (e.g., stock market returns) or income distribution, where a few high values may heavily influence the distribution.

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