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ASRB Agricultural Research Service Nonparametric & Rank-Based Methods: practice questions

ASRB Agricultural Research Service (ARS) - Agricultural Statistics scientist · India · Agricultural Scientists Recruitment Board (ICAR)

Original practice questions written to the published syllabus · not past papers
7worked here, free
27written to this exam in this subject
11syllabus units

What this subject covers

Methods that hold without a normal model: the empirical distribution, goodness-of-fit, sign and rank tests for one, two and many samples, rank correlation, and tests used for scored or ranked agricultural data such as disease ratings and sensory panels.

In the exams: AICE-JRF Ag Stat Unit 2 (heavy: run, sign, rank, Wilcoxon, Kruskal-Wallis, Mann-Whitney, Cochran, Friedman named); GATE ST 'Non-parametric Statistics' (heavy); UPSC ISS Statistics II (present)

Where the 27 questions fall

How our questions for ASRB Agricultural Research Service spread across the units of Nonparametric & Rank-Based Methods. Difficulty: 7 easy, 19 medium, 1 hard.

Analysing ordinal scores: disease ratings and sensory panels7
Sign test and Wilcoxon signed-rank test3
Friedman's test for randomised blocks3
Cochran's Q test3
Goodness of fit: chi-square and Kolmogorov-Smirnov tests2
Run test for randomness2
Mann-Whitney U test and two-sample Kolmogorov-Smirnov test2
Kruskal-Wallis test2
Empirical distribution function and its properties1
Spearman's and Kendall's rank correlations1
Asymptotic relative efficiency1

7 practice questions, worked

Choose an answer, then open the working. Each explanation says why the right answer is right and why the others are not.

1 · Goodness of fit: chi-square and Kolmogorov-Smirnov tests · easy

In a chi-square goodness-of-fit test of insect counts, several classes have expected frequencies below 5. What is the usual remedy?

  1. Replace the chi-square test by a t-test on the class frequencies
  2. Pool adjacent classes, then count df after pooling
  3. Add 0.5 to every observed count and keep all of the classes
  4. Drop those classes from the table and keep the original degrees of freedom
Answer and working

B. Pool adjacent classes, then count df after pooling

Small expected counts make the χ² approximation unreliable; adjacent classes are pooled and the df counted on the pooled classes. Dropping classes discards data and biases the test.

Further reading: Gibbons and Chakraborti, Nonparametric Statistical Inference; Conover, Practical Nonparametric Statistics; Hollander, Wolfe and Chicken, Nonparametric Statistical Methods

2 · Run test for randomness · medium

A row of 20 plants (10 diseased, 10 healthy) shows only 6 runs. What is the normal-approximation Z and the interpretation?

  1. Z = 2.30: too many runs, indicating regular alternation
  2. Z = −1.06: no evidence against randomness
  3. Z = −1.84: too few runs, indicating clustering along the row
  4. Z = −2.30: too few runs, indicating clustering of disease
Answer and working

D. Z = −2.30: too few runs, indicating clustering of disease

E(R) = 11, Var(R) = 4.737, Z = (6 − 11)/2.176 = −2.30, beyond −1.96: diseased plants occur in clumps, as with plant-to-plant spread. Dividing by the variance instead of the SD understates |Z|.

Further reading: Gibbons and Chakraborti, Nonparametric Statistical Inference; Conover, Practical Nonparametric Statistics; Hollander, Wolfe and Chicken, Nonparametric Statistical Methods

3 · Mann-Whitney U test and two-sample Kolmogorov-Smirnov test · medium

Six treated and six untreated plants are scored for disease; the Mann-Whitney U for the treated group is 4. What is the normal-approximation Z, ignoring ties and continuity correction?

  1. −4.67
  2. −2.72
  3. −2.24
  4. −0.359
Answer and working

C. −2.24

E(U) = 36/2 = 18, Var(U) = 36 × 13/12 = 39, Z = (4 − 18)/√39 = −2.24. Using 36/4 as the variance gives −4.67.

Further reading: Gibbons and Chakraborti, Nonparametric Statistical Inference; Conover, Practical Nonparametric Statistics; Hollander, Wolfe and Chicken, Nonparametric Statistical Methods

4 · Spearman's and Kendall's rank correlations · medium

A breeder wants to know whether variety rankings for yield are consistent between two environments. Which summary is most appropriate?

  1. The one-sample KS test on each environment
  2. Spearman's or Kendall's rank correlation
  3. Cochran's Q on the yields above the median
  4. The paired t-test on the two environment yield means
Answer and working

B. Spearman's or Kendall's rank correlation

Consistency of ranking (a rank-based view of crossover genotype-by-environment interaction) is measured by rank correlation. A paired t-test compares mean levels, not rankings.

Further reading: Gibbons and Chakraborti, Nonparametric Statistical Inference; Conover, Practical Nonparametric Statistics; Hollander, Wolfe and Chicken, Nonparametric Statistical Methods

5 · Analysing ordinal scores: disease ratings and sensory panels · easy

Ten panellists each rank four mango samples for sweetness. Which statistic summarises their agreement?

  1. The Kruskal-Wallis H
  2. Spearman's ρ between each pair of samples
  3. Cochran's Q
  4. Kendall's coefficient of concordance W
Answer and working

D. Kendall's coefficient of concordance W

W = χ²_F/[b(k − 1)] measures concordance among b judges ranking k items. Spearman's ρ compares two rankings only.

Further reading: Gibbons and Chakraborti, Nonparametric Statistical Inference; Conover, Practical Nonparametric Statistics; Hollander, Wolfe and Chicken, Nonparametric Statistical Methods

6 · Analysing ordinal scores: disease ratings and sensory panels · medium

Farmers in a participatory trial each rank the same five varieties. Which test compares the varieties?

  1. Kruskal-Wallis, with farmers as groups
  2. Cochran's Q on the rank values
  3. The two-sample KS test for each pair of farmers
  4. Friedman's test, with farmers as blocks
Answer and working

D. Friedman's test, with farmers as blocks

Each farmer ranks all varieties, so farmers are blocks and Friedman's test applies; Kendall's W measures agreement among farmers. Kruskal-Wallis assumes independent groups.

Further reading: Gibbons and Chakraborti, Nonparametric Statistical Inference; Conover, Practical Nonparametric Statistics; Hollander, Wolfe and Chicken, Nonparametric Statistical Methods

7 · Friedman's test for randomised blocks · easy

A blocked trial has many treatments but only three blocks. Which rank test weights blocks by their range of responses and is often more powerful than Friedman's test here?

  1. Page's test
  2. Cochran's Q test
  3. McNemar's test
  4. Quade's test
Answer and working

D. Quade's test

Quade's test ranks the blocks by their ranges and weights within-block ranks accordingly; with few blocks and many treatments it tends to beat Friedman. Page's test targets ordered alternatives.

Further reading: Gibbons and Chakraborti, Nonparametric Statistical Inference; Conover, Practical Nonparametric Statistics; Hollander, Wolfe and Chicken, Nonparametric Statistical Methods

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Read the subject

Nonparametric & Rank-Based MethodsThe full study manual, free ASRB Agricultural Research ServiceFormat, sections and how it has changed

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Nonparametric & Rank-Based Methods for other exams

CUET-PG · IndiaGATE · IndiaIIT JAM · IndiaICAR AICE-JRF/SRF · IndiaIndian Statistical Institute admission tests · IndiaASRB NET · IndiaState statistical services · IndiaCSIR-UGC NET · IndiaIndian Statistical Service Examination · IndiaICAR AIEEA-PG · IndiaSSC Combined Graduate Level · India

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