When analyzing datasets, it’s not enough to know measures of central tendency (mean, median, mode) and variability (variance, standard deviation).
Skewness: The Measure of Asymmetry
Definition: Skewness measures the degree and direction of asymmetry in a distribution around its mean.
Income distribution → Often positively skewed because most people earn average wages, but a small number of high earners stretch the tail to the right.
Exam scores → If most students score high but a few fail badly, the distribution is negatively skewed.
Real-Life Example:
Stock returns → Usually leptokurtic (heavy-tailed). This means extreme ups and downs occur more frequently than in a normal curve.
Heights of people → Typically close to mesokurtic, since extreme deviations are rare.
Uniform distribution → Often platykurtic (light-tailed), with fewer outliers.
Key Difference
Skewness → Tells us about the direction of data spread (left, right, or symmetric).
Kurtosis → Tells us about the intensity of tails (normal, heavy, or light).
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