ICAR AICE-JRF/SRF - Agricultural Statistics, Computer Application, Bioinformatics (PhD admission and fellowship) · India · National Testing Agency for ICAR
Original practice questions written to the published syllabus · not past papersThe statistician as research partner: framing the scientist's question, choosing design and sample size, power, avoiding pseudo-replication, pre-registration, analysis plans, reporting standards, the p-value debate, ethics, and writing and presenting results. Research-methodology courses in the Agribusiness and Veterinary tracks cover the social-science and clinical sides.
In the exams: Present in AICE-JRF research-methodology units of other subjects; ASRB ARS interview (reported); professional accreditation (RSS CStat, ASA PStat) evidence of practice (heavy)
How our questions for ICAR AICE-JRF/SRF spread across the units of Statistical Consulting, Research Methodology & Scientific Communication. Difficulty: 0 easy, 10 medium, 9 hard.
| Choosing the experimental or sampling unit | 3 | |
| Power and sample size for experiments and surveys | 3 | |
| Common errors in agricultural research: pseudo-replication and multiple testing | 3 | |
| Analysis plans and pre-registration | 2 | |
| P-values, confidence intervals and the ASA statement on p-values | 2 | |
| Research ethics, data integrity and authorship | 2 | |
| The consulting interview: from scientific question to statistical question | 1 | |
| Reporting standards for experiments and surveys | 1 | |
| Writing theses, papers and technical reports | 1 | |
| Project proposals and research funding | 1 |
Choose an answer, then open the working. Each explanation says why the right answer is right and why the others are not.
Each temperature regime is applied to one growth chamber containing 30 pots. Treatment replication is:
A. No replication
Pots in a chamber are subsamples, not replicates.
Further reading: Hurlbert (1984); Wasserstein and Lazar, ASA statement on p-values (2016); Mead, Gilmour and Mead, Statistical Principles for the Design of Experiments (2012); Cabrera and McDougall, Statistical Consulting (2002)
With σ = 0.5 t/ha, δ = 0.3 t/ha, α = 0.05 and 90% power, n ≈ 2(z₁ + z₂)²σ²/δ² gives about how many plots per treatment?
B. 59
2 × (1.96 + 1.28)² × 0.25/0.09 ≈ 58.4, rounded up to 59; 44 corresponds to 80% power.
Further reading: Hurlbert (1984); Wasserstein and Lazar, ASA statement on p-values (2016); Mead, Gilmour and Mead, Statistical Principles for the Design of Experiments (2012); Cabrera and McDougall, Statistical Consulting (2002)
To estimate a proportion within ±5% at 95% confidence by simple random sampling with p = 0.5, the sample size is about:
A. 385
1.96² × 0.25/0.05² ≈ 384.2, rounded up.
Further reading: Hurlbert (1984); Wasserstein and Lazar, ASA statement on p-values (2016); Mead, Gilmour and Mead, Statistical Principles for the Design of Experiments (2012); Cabrera and McDougall, Statistical Consulting (2002)
The design effect for clusters of m = 11 households with intra-cluster correlation ρ = 0.1 is:
D. 2.0
deff = 1 + (m − 1)ρ = 1 + 10 × 0.1.
Further reading: Hurlbert (1984); Wasserstein and Lazar, ASA statement on p-values (2016); Mead, Gilmour and Mead, Statistical Principles for the Design of Experiments (2012); Cabrera and McDougall, Statistical Consulting (2002)
With 20 independent tests of true null hypotheses at α = 0.05, the probability of at least one false positive is about:
D. 0.64
1 − 0.95²⁰ ≈ 0.64; one false positive is expected.
Further reading: Hurlbert (1984); Wasserstein and Lazar, ASA statement on p-values (2016); Mead, Gilmour and Mead, Statistical Principles for the Design of Experiments (2012); Cabrera and McDougall, Statistical Consulting (2002)
Bonferroni correction for 20 tests at an overall α of 0.05 tests each at:
D. 0.0025
Each test uses α/m = 0.05/20 = 0.0025.
Further reading: Hurlbert (1984); Wasserstein and Lazar, ASA statement on p-values (2016); Mead, Gilmour and Mead, Statistical Principles for the Design of Experiments (2012); Cabrera and McDougall, Statistical Consulting (2002)
The Benjamini–Hochberg procedure controls:
D. The false discovery rate
It suits large exploratory sets such as genomic scans.
Further reading: Hurlbert (1984); Wasserstein and Lazar, ASA statement on p-values (2016); Mead, Gilmour and Mead, Statistical Principles for the Design of Experiments (2012); Cabrera and McDougall, Statistical Consulting (2002)
A yield difference of 0.42 t/ha has SE 0.15. An approximate 95% confidence interval is:
D. 0.13 to 0.71 t/ha
0.42 ± 1.96 × 0.15; 0.27–0.57 uses ±1 SE.
Further reading: Hurlbert (1984); Wasserstein and Lazar, ASA statement on p-values (2016); Mead, Gilmour and Mead, Statistical Principles for the Design of Experiments (2012); Cabrera and McDougall, Statistical Consulting (2002)
A survey report omits the response rate. This matters because:
C. It reveals possible non-response bias
Readers need it to judge representativeness.
Further reading: Hurlbert (1984); Wasserstein and Lazar, ASA statement on p-values (2016); Mead, Gilmour and Mead, Statistical Principles for the Design of Experiments (2012); Cabrera and McDougall, Statistical Consulting (2002)
Repeatedly looking at accumulating trial data and stopping when p < 0.05 without adjustment:
A. Inflates false positives
Interim analyses must be planned and adjusted.
Further reading: Hurlbert (1984); Wasserstein and Lazar, ASA statement on p-values (2016); Mead, Gilmour and Mead, Statistical Principles for the Design of Experiments (2012); Cabrera and McDougall, Statistical Consulting (2002)
Ten here, 19 in the app. The Laisha app has every practice question we have written to ICAR AICE-JRF/SRF in this subject, marked as you go, with timed papers, a jump-to-unanswered list and a certificate when you pass at 70 per cent.
Free for ten days, then one subscription of US$10 a month. We would rather say so here than have you find it later.
An AI operating system for agriculture: free guides, manuals and tools written for every country.