Government Operational Research Service (GORS) Assessment Data Analysis, Modelling and Analytical Tools Flashcards

24 sample cards from the Data Analysis, Modelling and Analytical Tools deck

Sampled from the end of the deck, so these are different cards from the ones shown on the syllabus page.

1. Compare R and Python for government analytical work.

   R excels at statistics, official-statistics workflows, and visualisation (tidyverse, ggplot2, Shiny). Python is a general-purpose language strong in data wrangling (pandas), machine learning (scikit-learn), and production/automation. Both are open-source and endorsed for RAP.

2. In R's tidyverse, what do the core dplyr 'verbs' do?

   filter() selects rows by condition; select() picks columns; mutate() creates/modifies columns; arrange() sorts rows; summarise() aggregates; and group_by() defines groups for grouped operations. The pipe (%>% or |>) chains them.

3. What is the equivalent of R's data.frame in Python's pandas, and a key wrangling operation?

   The pandas DataFrame is the equivalent tabular structure. Common operations include groupby() for split-apply-combine aggregation, merge()/join for combining tables, and pivot_table() for reshaping.

4. Name three common Excel lookup/reference functions and what they do.

   VLOOKUP/XLOOKUP retrieve a value from a table by matching a key; INDEX returns a value at a given row/column position; MATCH returns the position of a lookup value. INDEX-MATCH (or XLOOKUP) is more flexible than VLOOKUP.

5. What is the difference between a relative and an absolute cell reference in Excel?

   A relative reference (e.g. A1) adjusts when the formula is copied to another cell. An absolute reference (e.g. $A$1) stays fixed. Mixed references (A$1 or $A1) lock only the row or only the column.

6. What is the UK government guidance on good spreadsheet practice?

   The 'Good practice guidance for the development of spreadsheets' / spreadsheet QA guidance (linked to the Aqua Book) recommends separating inputs, calculations and outputs, documenting assumptions, avoiding hard-coded values in formulas, using version control, and proportionate testing to reduce model error.

7. Name specialist OR and simulation software/techniques used in government analysis.

   Discrete-event simulation tools (e.g. Simul8, Arena, AnyLogic), system dynamics tools (e.g. Vensim, Stella), optimisation/LP solvers (e.g. CPLEX, Gurobi, open-source via Python PuLP/R), and Monte Carlo simulation. SimPy is a Python discrete-event library.

8. Distinguish discrete-event simulation from system dynamics.

   Discrete-event simulation models individual entities flowing through a system as discrete events over time (e.g. patients through a clinic, queueing) and is bottom-up/stochastic. System dynamics models aggregate stocks and flows with feedback loops at a high level, and is typically continuous and deterministic.

9. What is Monte Carlo simulation?

   A technique that repeatedly samples random values from input probability distributions, runs the model for each draw, and aggregates the outputs to estimate the distribution of results, capturing uncertainty and risk rather than a single point estimate.

10. State Edward Tufte's principles of graphical excellence / data-ink ratio.

    Maximise the data-ink ratio: $\text{data-ink ratio} = \frac{\text{ink used to show data}}{\text{total ink}}$. Show the data, avoid 'chartjunk' and unnecessary decoration, maximise data density, and tell the truth about the data (avoid distortion).

11. How should you choose a chart type for the data relationship you want to show?

    Comparison among categories: bar chart. Trend over time: line chart. Part-to-whole: stacked bar or (sparingly) pie. Distribution: histogram/boxplot. Relationship between two variables: scatter plot. Match the encoding to the message and the data type.

12. What is Tufte's 'lie factor' and how is it computed?

    $\text{Lie factor} = \frac{\text{size of effect shown in graphic}}{\text{size of effect in the data}}$. A value of 1 is honest; values far from 1 indicate the graphic distorts the data (e.g. truncated axes, 3D effects, varying area to show a 1D quantity).

13. List good-practice rules for accessible, honest charts in government.

    Start bar-chart axes at zero, label axes and units clearly, use colour-blind-safe palettes and not colour alone, provide alt text/data tables, keep it simple (high data-ink), order categories meaningfully, and follow the Government Statistical Service (GSS) data visualisation guidance.

14. What distinguishes a dashboard from a static report?

    A dashboard presents key metrics and visuals in a single consolidated, often interactive and regularly refreshed view, letting users filter and drill down. A static report is a fixed snapshot. Dashboards suit ongoing monitoring; reports suit one-off communication of a finished analysis.

15. Name common tools for building dashboards and interactive reporting in government.

    R Shiny, Python Dash/Streamlit, Power BI, Tableau, and Quarto/RMarkdown for interactive HTML. The GOV.UK style and accessibility requirements apply to public-facing dashboards.

16. What design principles make a dashboard effective?

    Lead with the most important KPI, use a clear visual hierarchy and logical layout (e.g. top-left first), keep it uncluttered, ensure consistent and meaningful use of colour, provide context (targets, trends, comparisons), and make it accessible and fast to load.

17. What is a confidence interval and how is a 95% interval interpreted?

    A confidence interval is a range, computed from data, that quantifies estimate uncertainty. A 95% CI means that if the procedure were repeated many times, about 95% of the intervals would contain the true parameter. For a mean: $\bar{x} \pm z_{0.025}\frac{s}{\sqrt{n}}$ (large samples).

18. What is a prediction interval and how does it differ from a confidence interval?

    A confidence interval bounds the uncertainty in an estimated parameter (e.g. the mean). A prediction interval bounds where a single future observation is likely to fall, so it is wider because it also includes individual-level variability, not just estimation uncertainty.

19. What visual methods communicate uncertainty in charts?

    Error bars, shaded confidence bands/fan charts (e.g. Bank of England inflation fan chart), violin/box plots showing distributions, gradient/blurred edges, and showing multiple plausible scenarios. The aim is to avoid presenting a single line as if it were certain.

20. How should analysts communicate uncertainty verbally to decision-makers?

    Use a consistent, calibrated probability language (e.g. the IPCC/PHIA likelihood scale: 'highly likely' ~90-100%, 'likely' ~55-75%, 'unlikely', etc.), state key assumptions and caveats clearly without burying the headline, and quantify ranges rather than implying false precision.

21. What is the core idea of 'storytelling with data' for decision-makers (per Cole Nussbaumer Knaflic)?

    Move from merely showing data to communicating a clear narrative: understand the context and audience, choose an effective visual, eliminate clutter, focus attention with preattentive attributes (e.g. colour, size), and tell a story with a clear 'so what' and call to action.

22. What are 'preattentive attributes' and why do they matter in data communication?

    Preattentive attributes (colour, size, position, orientation, shape, intensity) are visual properties the brain processes almost instantly, before conscious attention. Strategic use of one or two of them directs the audience's eye to the most important point in a chart.

23. Outline a structure for presenting an analytical finding to senior decision-makers.

    Lead with the bottom-line answer/recommendation (BLUF), give the essential supporting evidence and one clear visual, state key assumptions, caveats and uncertainty, then set out implications and options. Tailor depth to the audience and end with a clear call to action.

24. What is data minimisation and why is it important for government analysts?

    Data minimisation (a UK GDPR principle) means collecting and processing only the personal data that is adequate, relevant and limited to what is necessary for the stated purpose. It reduces privacy risk, breach impact, and the chance of unlawful or unfair processing.

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