The latest articles on DEV Community by richard nelson (@richardnel33254). https://dev.to/richardnel33254 https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F3581250%2F5edae3ce-8066-4d55-89e9-42b40621fffa.jpg https://dev.to/richardnel33254 en richard nelson Fri, 24 Oct 2025 06:38:15 +0000 https://dev.to/richardnel33254/understanding-skewness-and-kurtosis-2mpe https://dev.to/richardnel33254/understanding-skewness-and-kurtosis-2mpe <p>When analyzing datasets, it’s not enough to know measures of central tendency (mean, median, mode) and variability (variance, standard deviation).<br> Skewness: The Measure of Asymmetry<br> Definition: Skewness measures the degree and direction of asymmetry in a distribution around its mean.<br> Income distribution → Often positively skewed because most people earn average wages, but a small number of high earners stretch the tail to the right.<br> Exam scores → If most students score high but a few fail badly, the distribution is negatively skewed.<br> Real-Life Example:</p> <p>Stock returns → Usually leptokurtic (heavy-tailed). This means extreme ups and downs occur more frequently than in a normal curve.<br> Heights of people → Typically close to mesokurtic, since extreme deviations are rare.<br> Uniform distribution → Often platykurtic (light-tailed), with fewer outliers.<br> Key Difference</p> <p>Skewness → Tells us about the direction of data spread (left, right, or symmetric).<br> Kurtosis → Tells us about the intensity of tails (normal, heavy, or light).</p> analytics beginners datascience