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NDA Standard Deviation & Variance

Study Material — 8 PYQs (2018–2020) · Concept Notes · Shortcuts

NDA Standard Deviation & Variance is a frequently tested subtopic — 8 previous year questions from 2018–2020 papers are included below with concept notes, key rules and shortcut tricks.

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Previous Year Questions

NDA Standard Deviation & Variance — Past Exam Questions

8 questions from actual NDA papers · all shown free · click option to reveal solution

Exam Q 12019Previous Year Pattern

The variance of a dataset is 36. If each observation in the dataset is multiplied by 5, what will be the new variance?

Exam Q 22020Previous Year Pattern

The variance of a dataset is 36. If each observation in the dataset is multiplied by 5, what will be the new variance?

Exam Q 32018Previous Year Pattern

For the dataset {1, 3, 5, 7, 9}, the standard deviation is closest to:

Exam Q 42019Previous Year Pattern

The variance of a dataset is 36. If each observation in the dataset is multiplied by 5 and then 3 is subtracted from each result, what will be the new variance?

Exam Q 52020Previous Year Pattern

The variance of a dataset is 36. If each observation in the dataset is multiplied by 5 and then 3 is subtracted from each result, what will be the new variance?

Exam Q 62018Previous Year Pattern

If each observation in a data set is multiplied by 3, and the standard deviation of the original data set is 4, what is the variance of the new data set?

Exam Q 72018Previous Year Pattern

Two datasets have the same mean of 50 but different standard deviations. Dataset A has SD = 5 and Dataset B has SD = 10. If we increase every value in both datasets by 20, what will be the ratio of the new standard deviations?

Exam Q 82020Previous Year Pattern

Two datasets A and B have the same mean of 50. Dataset A consists of 5 observations with variance 16, while Dataset B consists of 10 observations with variance 25. If the two datasets are combined into a single dataset of 15 observations, what is the variance of the combined dataset?

Concept Notes

Standard Deviation & Variance— Rules & Concept

Core ConceptRead this first — the foundation of the topic

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

Formula BlockMemorise — at least one formula appears in every paper
Variance = Σ(x - mean)² / n
Standard Deviation = √Variance
For quick calculation: Variance = (Σx² / n) - (mean)²
Exam PatternsWhat examiners ask — read before attempting PYQs

SSC CGL typically asks: - Calculate SD or Variance from raw data - Compare spread of two datasets - Identify which dataset has more variation - Effect on SD when all values are multiplied or added by a constant SHORTCUT/TRICK: When values are multiplied by k: New SD = k × Original SD When values are added by c: New SD = Original SD (unchanged) This saves calculation time significantly.

Worked ExampleSolve this step-by-step before moving on
1
Step 1

Find mean = (2+4+6+8+10)/5 = 30/5 = 6

2
Step 2

Find differences from mean: (2-6)=-4, (4-6)=-2, (6-6)=0, (8-6)=2, (10-6)=4

3
Step 3

Square differences: 16, 4, 0, 4, 16

4
Step 4

Variance = (16+4+0+4+16)/5 = 40/5 = 8

5
Step 5

Standard Deviation = √8 = 2.83 (approximately)

Exam TrapsCommon mistakes students make — avoid these

Students forget to divide by n after summing squared differences. They calculate Σ(x-mean)² but stop there—this is NOT variance. You MUST divide by n.

Also, many confuse which measure to use; remember SD is more commonly reported in exams because it's interpretable.

Key Points to Remember

  • Variance measures average squared distance of data points from the mean; Standard Deviation is its square root.
  • Formula: Variance = Σ(x - mean)² / n; SD = √Variance
  • If all data values are identical, Standard Deviation = 0 (no spread).
  • When multiplying all values by k: New SD = k × Original SD; when adding c: SD remains unchanged.
  • Standard Deviation is preferred in exams because it's expressed in the same units as original data, making it more interpretable.
  • Larger SD indicates data is spread out; smaller SD indicates data is clustered around the mean.

Exam-Specific Tips

  • Standard Deviation formula: σ = √[Σ(x - μ)² / n], where μ is the arithmetic mean and n is number of observations.
  • Variance is the square of Standard Deviation: σ² = Variance.
  • Alternative variance formula for quick calculation: Variance = (Σx² / n) - (mean)².
  • Property: If each observation is multiplied by constant k, new SD = k × original SD (linear transformation rule).
  • Property: If constant c is added to each observation, SD remains unchanged (addition does not affect spread).
  • Standard Deviation of any dataset is always a non-negative value (σ ≥ 0).
  • Coefficient of Variation (CV) = (SD / Mean) × 100, used to compare variability across different datasets with different means.
  • For grouped data, class midpoints are used in calculations, and frequency weights are applied in variance formula.
Practice MCQs

Standard Deviation & Variance — Practice Questions

52graded MCQs · easy to hard · full solution & trap analysis · showing 20 of 52

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Practice 1easy

The variance of the dataset {1, 2, 3, 4, 5} is:

Practice 2easy

The standard deviation of the dataset {2, 4, 6, 8, 10} is 2√2. What is the variance of this dataset?

Practice 3easy

The standard deviation of the dataset {5, 5, 5, 5, 5} is:

Practice 4easy

The mean of five numbers is 12 and the sum of their squared deviations from the mean is 80. What is the standard deviation?

Practice 5easy

The variance of the dataset {2, 4, 6, 8, 10} is:

Practice 6easy

If the variance of a dataset is 16, what is the standard deviation?

Practice 7easy

Five students scored: 10, 20, 30, 40, 50 marks. The mean is 30. What is the variance of their scores?

Practice 8easy

If the standard deviation of a dataset is 5, what is the variance?

Practice 9easy

The standard deviation of the dataset {3, 6, 9} is approximately:

Practice 10easy

If the standard deviation of a dataset is 5, what is the variance?

Practice 11easy

The variance of the dataset {2, 4, 6, 8, 10} is:

Practice 12easy

A dataset has a standard deviation of 5. If each value in the dataset is multiplied by 3, what will be the new standard deviation?

Practice 13easy

The standard deviation of a dataset is 4. If each data point is multiplied by 3, the new standard deviation will be:

Practice 14easy

The variance of the dataset {5, 5, 5, 5, 5} is:

Practice 15easy

The variance of the dataset {3, 6, 9} is:

Practice 16easy

The variance of a dataset is 36. What is the standard deviation of this dataset?

Practice 17easy

If each value in a dataset is increased by 7, the standard deviation will:

Practice 18medium

The standard deviation of five numbers is 6. If 4 is added to each number, what is the new standard deviation?

Practice 19medium

A dataset has mean 25 and standard deviation 5. If 10 is subtracted from each observation, what will be the new standard deviation?

Practice 20medium

The mean of a dataset is 50 and the standard deviation is 10. If the variance is 100, what is the coefficient of variation (as a percentage)?

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60-Second Revision — Standard Deviation & Variance

  • Formula: Variance = Σ(x-mean)²/n; SD = √Variance. Use quick formula: Variance = (Σx²/n) - (mean)² to save time.
  • Multiply by k → SD multiplies by k; Add constant c → SD unchanged. Use this trick for transformation questions.
  • SD=0 only when all values are identical. Higher SD = more scattered; Lower SD = clustered around mean.
  • Trap: Don't forget to divide by n after summing squared differences—this is the most common error in calculations.
  • Remember: SD is preferred over Variance in SSC exams because it's in original units and easier to interpret.
  • For two datasets with same mean: compare SDs to determine which is more variable/consistent.
  • In exam, if asked 'which dataset varies more?'—calculate or compare SD values; higher SD = more variation.
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