Skewness Essential terms you should know.

Skewness A Term You Need to Know

Skewness is a measure of the asymmetry of the probability distribution of a random variable about its mean. In other words, it describes how much a data set deviates from a symmetrical distribution. A data set is considered symmetrical if the values to the left and right of the mean are roughly the same. If a data set is skewed to the left, it is said to be negatively skewed, while a data set skewed to the right is positively skewed.

Skewness can be quantified using various mathematical formulas, but it is generally measured by comparing the mean and median of the data set. If the mean and median are equal, the data set is considered symmetrical. If the mean is greater than the median, the data set is positively skewed, and if the mean is less than the median, the data set is negatively skewed.

Skewness is an important factor to consider when analyzing a data set, as it can affect the interpretation of other statistical measures such as the mean and standard deviation. A skewed data set may also have outliers that can impact the results of statistical tests and affect the validity of inferences made from the data.

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