Question
The mean of observations is . If each observation is multiplied by and then is subtracted from each, what is the new mean? Then, if the standard deviation of the original data was , what is the new standard deviation? This kind of “linear transformation of data” question is a CBSE Class 10/11 staple.
Solution — Step by Step
If each observation becomes , then:
Here , , :
For :
Notice: the additive constant does not affect the spread (it just shifts everything by the same amount). Only the multiplicative factor matters.
Plug in: .
If original data were (mean , SD ), transformed to (mean , SD ). Mean ratio simple, but ✓. SD ratio ✓.
Why This Works
Mean is a linear statistic — it commutes with linear operations. Add a constant to every data point, mean increases by that constant. Multiply every data point, mean multiplies the same way.
Standard deviation measures spread around the mean. Adding a constant shifts the whole distribution but doesn’t stretch or shrink it — spread unchanged. Multiplying scales every deviation from the mean by , so SD scales by . The absolute value matters because SD is always non-negative even if is negative.
Alternative Method
Use the variance formula directly. Substitute and :
Hence . This derivation is the kind of result CBSE asks in 4-mark questions.
Whenever you see a “change of scale and origin” problem, immediately apply: new mean = , new SD = , new variance = . These three formulas knock out half the statistics PYQs in seconds.
Common Mistake
Two errors trap students:
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Adding to the SD. Wrong — only scales SD; shifts the mean only. If the question subtracts from every value, SD stays the same magnitude.
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Forgetting the absolute value on . If , then , not . SD is non-negative by definition. The negative sign would flip the data, but spread is invariant under reflection.
The other common slip: confusing variance and standard deviation under transformation. Variance scales as ; SD scales as . Always check what the question asks for.
Final answer: New mean , new standard deviation .