ICAR-IASRI MSc and PhD (Agricultural Statistics, Computer Application, Bioinformatics) · India · ICAR-Indian Agricultural Statistics Research Institute with the IARI Graduate School
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-IASRI MSc and PhD spread across the units of Statistical Consulting, Research Methodology & Scientific Communication. Difficulty: 16 easy, 12 medium, 0 hard.
| Power and sample size for experiments and surveys | 4 | |
| Choosing the experimental or sampling unit | 3 | |
| Analysis plans and pre-registration | 3 | |
| Common errors in agricultural research: pseudo-replication and multiple testing | 3 | |
| Reporting standards for experiments and surveys | 3 | |
| Writing theses, papers and technical reports | 3 | |
| P-values, confidence intervals and the ASA statement on p-values | 2 | |
| Research ethics, data integrity and authorship | 2 | |
| Presenting statistical results to non-statisticians | 2 | |
| Project proposals and research funding | 2 | |
| The consulting interview: from scientific question to statistical question | 1 |
Choose an answer, then open the working. Each explanation says why the right answer is right and why the others are not.
A useful question for identifying the experimental unit is:
C. What was randomised to what?
The unit of randomisation defines the experimental unit.
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 cluster survey has design effect 2. Compared with simple random sampling, the sample needed for the same precision is:
D. Twice as large
Multiply the SRS size by deff.
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)
For a randomised block design with many treatments and blocks, power is most flexibly obtained by:
C. Simulation of the planned design
Simulation handles complex structures.
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)
Writing an analysis plan early often:
D. Reveals design problems early
Planning the analysis tests the design.
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)
Hurlbert's 1984 term for treating non-independent subsamples as replicates is:
B. Pseudo-replication
It inflates apparent significance.
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)
Reporting guidelines exist mainly because:
D. Many reports omit essentials
Missing details prevent evaluation and replication.
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)
Error bars in a figure must be:
B. Defined in the caption
Readers cannot interpret undefined bars.
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)
Replacing 'significant at p < 0.05' when speaking to farmers, a better statement is:
A. How big, and how sure
Size and certainty matter for decisions.
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)
Objectives in a research proposal should be:
A. SMART
SMART objectives can be evaluated.
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 theory of change describes:
B. How a project leads to impact
Funders use it alongside logframes.
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, 28 in the app. The Laisha app has every practice question we have written to ICAR-IASRI MSc and PhD 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.