A set of n values has a standard deviation of 5. The standard deviation of n values will be:
- AOption (a)
0
- BOption (b)
5
- COption (c)
10
- DOption (d)
7
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Correct answer
5
Why this answer is correct
Shortcut Trick
The standard deviation of any given data set is completely independent of the change of origin.
This means adding or subtracting a constant value from all observations does not change the standard deviation, as the overall spread of the data remains the same.
Since each original value is uniformly decreased by 7, the new standard deviation will be exactly equal to the original standard deviation.
∴ The correct answer is 5.
Alternate Method
Given:
Standard deviation of n values X1, X2, ..., Xn = 5
New values: X1 − 7, X2 − 7, ..., Xn − 7
Formula Used:
If a new dataset is formed by the linear transformation Yi = aXi + b, then SD(Y) = |a| × SD(X).
Calculations:
⇒ In the new series, each value is generated by shifting the original values by −7.
⇒ Comparing with the general transformation Yi = aXi + b, we get a = 1 and b = −7.
⇒ New Standard Deviation = |1| × Original Standard Deviation
⇒ New Standard Deviation = 1 × 5 = 5
⇒ The subtraction of a constant solely shifts the mean position of the data curve but does not affect the inner dispersion (spread) of the elements.
∴ The correct answer is 5.
Additional Information
Change of Origin (Addition/Subtraction)
Standard deviation and variance are completely independent of the change of origin. Adding or subtracting a constant value to all data points shifts the central mean but perfectly preserves the gaps between the data points.
Change of Scale (Multiplication/Division)
Standard deviation is heavily dependent on the change of scale. If every single observation is identically multiplied or divided by a constant 'k', the new standard deviation inherently becomes |k| times the original standard deviation.
Variance vs Standard Deviation
Variance mathematically behaves as the square of the standard deviation (σ2). Therefore, while the standard deviation formally scales by a factor of |k|, variance fundamentally scales by a factor of k2 when values are multiplied by 'k'.




























