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))
📊 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.


Top comments (0)