Standard Deviation and Variance are measures that tell us how spread out data is from the average. Think of them as measuring 'how different' the numbers are from each other.
CORE CONCEPT:
Imagine you have marks of 5 students: 10, 20, 30, 40, 50. All are spread out widely.
Now imagine: 28, 29, 30, 31, 32. These are clustered tightly around 30. Both have the same average (30), but the spread is different.
Variance and Standard Deviation measure this spread.
Variance (σ²) = Average of squared differences from the mean
Standard Deviation (σ) = Square root of Variance
KEY RULES:
1. Standard Deviation is always non-negative (≥0)
2. If all numbers are identical, SD = 0
3.
Larger SD means more scattered data; smaller SD means data is clustered
4. Standard Deviation is preferred over Variance because it's in the same units as original data