Understanding Skewness and Kurtosis: A Friendly Guide for Data Enthusiasts

If you've ever looked at a histogram and thought, "Hmm... this looks weirdly stretched or tilted," you're not alone. What you're noticing might be skewness or kurtosis — two important concepts in statistics that describe the shape of a distribution.

📈 What is Skewness?

Skewness tells us about the asymmetry of a distribution.

positive skewed:

Tail stretches more on the right. Mean > Median.

negatively skewed:

Tail stretches more on the left. Mean < Median.

Zero skewness:

Perfectly symmetrical (like a normal distribution).

💡 Example in Python:

import scipy.stats as stats
import numpy as np
data = np.random.exponential(scale=2, size=1000)
print("Skewness:", stats.skew(data))

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📊 What is Kurtosis?

Kurtosis is a statistical measure that describes the “tailedness” of a distribution — in other words:

• How heavy or light the tails of your data are compared to a normal distribution.

Types of Kurtosis:

Mesokurtic

• Normal distribution (reference standard)
• its value is equal to 3

Leptokurtic

• Heavy tails (more outliers); sharper peak
• its value is more than 3

Platykurtic

• Light tails (fewer outliers); flatter, wider peak
• its value is less than 3

🔹 In Python (e.g., scipy.stats.kurtosis()), the default subtracts 3 (so normal = 0).

This is called excess kurtosis.