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:
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 measures the "tailedness" of the distribution — how extreme the outliers are.
High kurtosis (leptokurtic):
Heavy tails (more outliers).
Low kurtosis (platykurtic):
Light tails (fewer outliers).
Normal kurtosis (mesokurtic):
~3 (for normal distribution).
💡 Python Example:
print("Kurtosis:", stats.kurtosis(data, fisher=False))
`import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import kurtosis, skew
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