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ICAR AIEEA-PG Nonparametric & Rank-Based Methods: practice questions

ICAR AIEEA-PG - Group 07 Statistical Sciences (Master's admission and PG scholarship) · India · National Testing Agency for ICAR

Original practice questions written to the published syllabus · not past papers
6worked here, free
21written to this exam in this subject
10syllabus 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 21 questions fall

How our questions for ICAR AIEEA-PG spread across the units of Nonparametric & Rank-Based Methods. Difficulty: 18 easy, 3 medium, 0 hard.

Goodness of fit: chi-square and Kolmogorov-Smirnov tests3
Spearman's and Kendall's rank correlations3
Analysing ordinal scores: disease ratings and sensory panels3
Empirical distribution function and its properties2
Run test for randomness2
Sign test and Wilcoxon signed-rank test2
Kruskal-Wallis test2
Cochran's Q test2
Mann-Whitney U test and two-sample Kolmogorov-Smirnov test1
Friedman's test for randomised blocks1

6 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 · easy

In a runs test for randomness, what do far too many runs suggest?

  1. Clustering of like symbols or a trend in the series
  2. Unequal variances
  3. A shift in location
  4. Alternation or negative dependence
Answer and working

D. Alternation or negative dependence

Many short runs mean the two symbols alternate more than chance allows, as with regular spacing or negative serial correlation. Too few runs indicate clustering or trend.

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 · easy

Two independent samples have sizes 6 and 6, and sample 1's rank sum is W₁ = 25. What is the Mann-Whitney statistic U₁?

  1. 4
  2. 11
  3. 25
  4. 14
Answer and working

A. 4

U₁ = W₁ − n₁(n₁ + 1)/2 = 25 − 21 = 4. U₂ = n₁n₂ − U₁ = 32. 14 = 25 − 11 omits the correct offset.

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

4 · Friedman's test for randomised blocks · easy

Which design is Friedman's test intended for?

  1. A completely randomised design with unequal group sizes
  2. A paired comparison of binary outcomes
  3. An RCBD with one observation per treatment per block
  4. A two-sample comparison of independent groups
Answer and working

C. An RCBD with one observation per treatment per block

Friedman ranks treatments within blocks, the rank analogue of the RCBD ANOVA. Binary blocked data use Cochran's Q.

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

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Nonparametric & Rank-Based MethodsThe full study manual, free ICAR AIEEA-PGFormat, 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 · IndiaASRB Agricultural Research Service · IndiaIndian Statistical Service Examination · IndiaSSC Combined Graduate Level · India

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