Shape measures describe the data distribution (or pattern) within a dataset. The 'low' and'high' end values on the x-axis of the histogram can be distinguished, and the distribution form of quantitative data can be characterised as there is a logical order to the values.
Because the median is more resistant to outliers than the mean, it is generally the chosen measure of central tendency for distributions with outliers or skewed distributions. The direction of skewness has an impact on the order of the mean, median, and mode, as seen below.
An outlier is a data point that doesn't fit into the overall pattern of a dataset. The term "outlier" refers to any observation in a dataset that falls outside of its expected range and is not representative of the whole population. Outliers could be due to errors, or they may reflect some genuine phenomenon not captured by other data points in the dataset.
Skewness is a measure of how far the distribution from the mean is from being symmetrical. It can be measured using the skewness coefficient, which has a range from -1 to 1.
The skewness of a data set is a measure of the degree of asymmetry in the probability distribution. It is calculated by finding the difference between the largest and smallest value in a data set, then dividing it by that difference.
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