Government and public policy · suite apt-003-policy-and-program-analysis · generated 2026-09-15T13:35:35.922Z
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This file contains the whole study outline for this suite: every skill it draws on, the full lesson for each of those skills, worked examples, practice tips, a glossary, and where each piece of material comes from. Nothing here is a summary of a page you still have to visit.
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| Mode | Duration | What it is for |
|---|---|---|
| Guided practice | 15 minutes | Untimed, with feedback after every item. |
| Mini-test | 18 minutes | A short timed set for checking pace. |
| Full simulation | 45 minutes | Full length and full time, in one sitting. |
This suite draws on 5 skill constructs. Each one below carries its complete lesson.
Tables, charts, graphs, dashboards, trends, comparisons, and evidence-based conclusions.
Data interpretation is the discipline of getting a correct number out of a table, chart or dashboard that was not built to make your question easy, and of saying so when the data cannot answer it at all. It dominates graduate and analyst screening, and it is the section where strong arithmetic still fails, because the marks are lost in the header row, the axis scale and the wording of the question. The same skill is the daily work of anyone who reports on a management pack, a clinical audit or a stock ledger. Practice is stored on this device only; there is no account and nothing is uploaded unless you export it.
What you should be able to do after this lesson:
Read the header: (£000) changes every answer by a factor of a thousand: Table titled 'Regional revenue, year to March (£000)': North 1,240; South 986; East 1,455; West 719. Total = 1,240 + 986 + 1,455 + 719 = 4,400, so the business turned over 4,400 thousand pounds, that is 4.4 million. East's share is 1,455 / 4,400 = 33.1 percent. The whole set: North 28.2 percent, South 22.4, East 33.1, West 16.3, summing to 100. Two things go wrong here. The first is reading East's revenue as 1,455 pounds and then reporting a business with a total turnover of 4,400 pounds, which nobody notices because every option is scaled the same way, until the question asks for revenue in millions and only one option is right. The second is the comparison wording. East's share is (1,455 - 719) / 4,400 = 16.7 percentage points above West's, and East's revenue is (1,455 - 719) / 719 = 102 percent more than West's, that is slightly more than double. 'Sixteen point seven' and 'a hundred and two' both describe the same two cells honestly, and the question decides which one is correct. Note that the percentage-point figure must be computed from the unrounded shares rather than by subtracting the rounded ones, or the last digit will not survive.
One pair of rows, three correct increases: A complaints table: 2023, 120,000 orders, complaint rate 4.0 percent; 2024, 150,000 orders, complaint rate 5.0 percent. Three defensible answers to 'how much did complaints increase?'. The rate rose by 1.0 percentage point. The rate rose by (5.0 - 4.0) / 4.0 = 25 percent in relative terms. And the count of complaints rose from 0.04 x 120,000 = 4,800 to 0.05 x 150,000 = 7,500, which is (7,500 - 4,800) / 4,800 = 56.25 percent. All three are arithmetically right; only one answers the question in front of you. The pattern to internalise is that a rate and a count move together only when the denominator is fixed, and here it is not. Order volume grew 25 percent as well. If the question is about customer experience, the rate is the honest figure; if it is about how many complaint handlers to hire, the count is. Test items usually ask for the one you would not have chosen.
Index numbers: five points is not five percent: A cost index with 2020 = 100 reads 104 in 2021, 111 in 2022 and 109 in 2023. From 2021 to 2023 the index rose 5 points, but the percentage change is 5 / 104 = 4.8 percent, because the base for the comparison is 104, not 100. From 2022 to 2023 it fell 2 points, which is -2 / 111 = -1.8 percent, and note that costs fell even though the index remains 9 percent above the 2020 base. A level and a change are different claims. The only comparison where points and percent coincide is against the base year itself: 2020 to 2023 is 100 to 109, exactly plus 9 percent. Watch also for a rebased series, where a table switches to 2022 = 100 partway down; the two segments cannot be compared directly without converting one of them, and an item that quietly rebases is testing whether you read the column heading.
Joining two tables: totals and per-head figures disagree on purpose: Table 1, headcount by site: Leeds 84, Derby 47, Bristol 129. Table 2, absence days recorded in the same period: Leeds 630, Derby 300, Bristol 903. 'Which site has the worst absence problem?' On raw totals Bristol is worst at 903 days. Normalise per head and the ranking changes: Leeds 630 / 84 = 7.50 days per employee, Bristol 903 / 129 = 7.00, Derby 300 / 47 = 6.38. Leeds is worst, Bristol is merely biggest. The organisation-wide figure is 1,833 / 260 = 7.05 days per head, which is a useful reference line: Leeds is above it, the other two below. The general rule is that any comparison between units of different size demands a denominator, and the denominator has to come from the other table. Items are built so the raw-total answer and the per-head answer are both on the option list, and so the site with the biggest total is never the site with the highest rate.
The truncated axis: measure the numbers, not the bars: A quarterly satisfaction chart with a y-axis running from 78 to 82 shows bars at 79.2, 79.8, 80.4 and 81.1. Visually the last bar looks several times taller than the first, because only the top 4 points of a 100-point scale are drawn. The actual movement is 81.1 - 79.2 = 1.9 points, which on the score's own scale is a relative rise of 1.9 / 79.2 = 2.4 percent. If the question asks 'by approximately what percentage did satisfaction improve', the answer is about 2 percent, and the distractor built from the bar heights will be something like 40 or 400 percent. Related presentation effects to check before answering: a dual-axis chart where two series use different scales and appear to cross meaningfully when they do not; a cumulative series, where a flattening line still means the total is growing, just more slowly; and a logarithmic axis, where equal vertical distances are equal ratios rather than equal amounts. In every case the defence is the same. Find the printed numbers, or read the gridline values, and compute.
Cannot say: revenue is not profit: A product table shows units sold and total revenue. Product P: 4,200 units, 71,400 pounds. Product Q: 1,800 units, 41,400 pounds. Average selling price is 71,400 / 4,200 = 17.00 for P and 41,400 / 1,800 = 23.00 for Q, so Q earns more per unit while P earns more in total. Now the statement to evaluate: 'P is more profitable than Q.' The correct response is cannot say. Profit needs cost, and the table has no cost column; a product with a 17 pound price and a 16 pound unit cost is less profitable than one priced at 23 with a cost of 9, and nothing here rules that out. Contrast with 'Q generated more revenue per unit than P', which the table fully supports and which is true. The habit worth building is to finish every cannot-say judgement with the missing input named out loud ('cannot say, because unit cost is not given') because that forces you to distinguish a genuinely unanswerable item from one you simply have not worked hard enough on.
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Evaluating evidence, assumptions, arguments, credibility, and alternative explanations.
Critical thinking assessments ask you to take an argument apart: what is being concluded, what it rests on, what evidence would settle it, and whether a proposed conclusion actually follows. Published critical-thinking batteries typically run five task types - inference, recognition of assumptions, deduction, interpretation, and evaluation of arguments - and they are deliberately built so that agreeing with a conclusion and judging the argument as strong come apart. The construct is heavily weighted in policy analysis, audit, legal, investigative and graduate selection, and it is the same skill that stops a plausible chart from turning into a bad decision. Practice stays on this device; there is no account and nothing is uploaded unless you export it.
What you should be able to do after this lesson:
The negation test finds assumptions; nothing else does: Argument: 'The council should fit bin sensors across the district. In the trial depot they cut collection trips by a fifth.' Candidate assumption A: 'The trial depot's waste pattern is broadly representative of the rest of the district.' Negate it - suppose the trial depot is nothing like the rest of the district - and the argument collapses, because a result that does not transfer supports nothing about the district. So A is a required assumption. Candidate assumption B: 'Bin sensors are the cheapest available technology for this purpose.' Negate it - suppose they are the most expensive - and the argument still stands as given, since it argued from a reduction in trips, not from cost. B is not required; it would strengthen a cost-based argument that was never made. Candidate C: 'Fewer collection trips reduce total operating cost.' Negate it and the recommendation loses its point, so C is required too, even though the argument never mentions cost. That last case is the one candidates miss, because the required assumption is what bridges the evidence to the recommendation, and bridging assumptions are by definition unstated. The failure mode across this whole item type is picking statements that support the conclusion instead of statements the argument cannot survive without.
Base rates: 90 percent accurate, 15 percent right: A production line runs an automated defect test. Defects occur in 1 percent of units. The test flags 90 percent of genuinely defective units, and wrongly flags 5 percent of good units. A unit has just been flagged - how likely is it to be defective? Work in whole units rather than probabilities. Take 10,000 units: 100 are defective and 9,900 are good. Of the 100 defective, the test flags 90. Of the 9,900 good, it wrongly flags 5 percent, which is 495. Total flags: 90 + 495 = 585. Of those, 90 are genuine, so the answer is 90 / 585 = 15.4 percent. Better than the 1 percent base rate, and nowhere near the 90 percent that intuition offers. The error has a name - confusing the probability of a flag given a defect with the probability of a defect given a flag - and it has a practical consequence: the rework queue has to be sized for 585 units a batch, not 100, and 495 of the 585 units sitting in it are perfectly good. Whenever a test, screening rule or model accuracy figure appears in an item, ask how many of the negatives there are, because with a rare condition the false positives from a large clean population swamp the true positives from a small affected one.
Three alternative explanations, and the comparison that settles it: Claim: 'Employees who attended the optional resilience workshop took 30 percent fewer sick days last year, so the workshop works.' Alternative one, selection: attendance was optional, so the people who signed up may have been the healthier and more engaged to begin with, and would have taken fewer sick days regardless. Alternative two, reverse direction: employees who were frequently unwell were the least able to attend a full-day workshop, so illness determined attendance rather than the other way round. Alternative three, a common third factor: the workshop ran on Thursday afternoons at head office, so attendance largely tracks being an office-based rather than shift-based worker, and shift workers take more sick leave for reasons that have nothing to do with resilience training. What would settle it: randomly assigning the offer to half the workforce and comparing the two groups. Failing that, the cheapest useful check already exists in the payroll data - compare attendees' and non-attendees' sick days in the year before the workshop. If attendees were already 30 percent lower, selection is doing the entire job. The habit worth building is to name the comparison group before you accept any before-and-after number.
Deduction: affirming the consequent versus modus tollens: Rule: 'If an invoice is over 10,000 euro, it requires two signatures.' Argument A: 'This invoice has two signatures, therefore it is over 10,000 euro.' Invalid - this is affirming the consequent. The rule makes two signatures necessary for large invoices; it says nothing that prevents a cautious manager from double-signing a 400 euro invoice, so a two-signature invoice of any size is consistent with the rule. Argument B: 'This invoice has only one signature, therefore it is not over 10,000 euro.' Valid - this is modus tollens, denying the consequent to deny the antecedent, and it holds assuming the rule was followed. Argument C: 'This invoice is not over 10,000 euro, therefore it does not have two signatures.' Invalid again, denying the antecedent. Two further points that deduction items test directly. First, validity is about form alone: 'All banks close on Sundays; Riverton Mutual is a bank; therefore Riverton Mutual closes on Sundays' is perfectly valid even if the first premise is false, and a valid argument with a false premise can deliver a false conclusion. Second, in these items you must accept the premises as given even when you know them to be untrue, because the question is whether the conclusion follows, not whether it is true.
Interpretation: what a survey figure does and does not license: Data: all 1,200 employees of a firm were surveyed. 62 percent said they would accept a four-day week at 90 percent pay. Of those who said yes, 71 percent were under 35. Conclusion one: 'A majority of this firm's employees would accept the trade.' This follows - 62 percent of a complete census of the firm is a majority, and the conclusion is properly limited to this firm. Conclusion two: 'A majority of the firm's under-35 employees would accept the trade.' This does not follow, and the arithmetic shows why. Sixty-two percent of 1,200 is 744 accepters, and 71 percent of 744 is about 528 accepters aged under 35. That is the composition of the accepters, not the acceptance rate within the under-35 group, and without knowing how many under-35s the firm employs the rate is undetermined: if there are 900, the rate is 528 / 900 = 59 percent; if there are 600, it is 88 percent; if there were 1,100 the rate would be below half. Conclusion three: 'The firm should move to a four-day week.' This does not follow either - a stated preference about pay says nothing about output, shift cover, customer hours or cost. Notice that conclusion two is the base-rate error from the screening example, wearing survey clothing.
Strong and weak arguments on the same question: Question: should the agency publish raw inspection scores for every site? Strong argument for: 'In the neighbouring authority, publication was followed by a measurable fall in repeat violations, which is the stated aim of the inspection regime.' It is directly about this decision, it addresses the regime's own objective, and it offers evidence - and if true it would change your view. Weak argument for: 'Yes, because transparency is always good.' Sweeping, unsupported, and it would apply identically to publishing anything at all, which is the tell. Strong argument against: 'Raw scores omit the severity weighting, so a site with one critical failure can rank above a site with six minor ones, and a member of the public cannot see the difference.' Specific, mechanistic, directly about the proposal, and it would change your view. Weak argument against: 'No, because businesses will object.' It may be true and it may matter politically, but it does not bear on whether publication achieves the regime's aim, and no evidence is offered. Two tests separate strong from weak: is the argument about the exact question asked, and would accepting it change the decision? Note that you can hold a firm personal view on publication and still be required to mark the argument on the other side as the strong one - that split is what the item type is measuring.
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Understanding passages, instructions, policies, technical material, and workplace documents.
Reading comprehension is the ability to take a passage, a policy clause, a procedure or a technical note and answer accurately about what it says, what it means, and how it applies to a case in front of you. It is assessed directly in casework, policy analysis, immigration and language-focused batteries, and it sits underneath almost every other written test as the thing that has to work before anything else can. On the job it is the difference between applying an eligibility rule correctly the first time and generating an appeal. Everything you practise here is stored on this device; there is no account and no upload.
What you should be able to do after this lesson:
Main idea: scope, not truth: Passage: 'When the Kirkwall depot adopted route-optimisation software in 2021, its drivers covered 9 percent fewer kilometres in the first quarter, and fuel spending fell by a similar margin. Delivery times improved on urban routes only; rural routes were unchanged, and two drivers reported longer split shifts. The depot manager has kept the software but now overrides its rural schedules by hand.' Which option states the main idea? (a) 'Route-optimisation software reduces fuel costs.' True, and it is exactly one third of the passage - it drops the mixed results and the override, so it is too narrow. (b) 'The software failed at Kirkwall.' Contradicted: mileage and fuel both improved, and the depot kept it. (c) 'Rural deliveries are harder to optimise than urban ones.' A generalisation the passage never makes; it reports one depot, one year, without explaining why rural routes were unchanged. Too broad. (d) 'At one depot the software produced clear mileage and fuel savings but uneven results elsewhere, so it is now used with manual override.' Correct - it covers all three sentences and no more. The lesson generalises: main-idea distractors are usually wrong for scope, and the two commonest failures are a true detail promoted to the main point, and a reasonable-sounding claim the passage never actually makes.
Reading a clause exactly: and, at least, more than, unless: Rule: 'An employee may claim the remote-working allowance if they work from home on at least three scheduled days per week AND their home is more than 40 km from their assigned office, unless they already receive the travel-card subsidy.' Four cases. Priya works from home four days, lives 52 km away, and receives the travel-card subsidy: not eligible - she satisfies both qualifying conditions, and the 'unless' clause overrides both. Tom works from home three days, lives 38 km away, no subsidy: not eligible, because the connective is AND and 38 is not more than 40. Dan works from home two days, lives 60 km away, no subsidy: not eligible, failing the days condition for the same structural reason. Mira works three days, lives 41 km away, no subsidy: eligible - 'at least three' includes exactly three, and 41 is more than 40. Three separate traps live in one sentence: reading AND as OR under time pressure, treating 'more than 40' as including 40, and forgetting that an exception clause outranks the conditions it follows. The reliable method is to rewrite the rule as a checklist - condition 1, connective, condition 2, then exception - before you look at any case.
Vocabulary in context: the evidence is in the next clause: Sentence: 'The auditor's remarks were characteristically dry, and the board took a full minute to realise she had been criticising them.' Which meaning does 'dry' carry here? (a) arid or lacking moisture, (b) dull and lacking interest, (c) understated and quietly ironic, (d) free of alcohol. Option (b) is the distractor that catches most people, because it is the commonest figurative sense and it half-fits an auditor. But the clause after 'and' is doing the work: a remark that takes a minute to land as criticism is understated, not boring - dullness would not delay recognition, it would only reduce attention. The answer is (c). The general method for vocabulary-in-context items is to ignore the word for a moment, read the rest of the sentence as evidence, and ask what property the sentence requires the word to have. Test items are built so that the most frequent sense of the word is available as an option and is wrong; if the most obvious meaning were correct, the item would measure nothing.
Referents: the former, the latter, and the noun three sentences back: Passage: 'Two remedies were proposed: a fixed surcharge on late filings, and a sliding penalty tied to the amount owed. The committee rejected the former on fairness grounds, noting that it would fall hardest on the smallest filers.' Which remedy was rejected? 'The former' is the first item mentioned, so the fixed surcharge. Candidates who skim choose the sliding penalty because it sits nearer to the pronoun, which is exactly why the item is written this way. Notice also the free consistency check built into the sentence: a fixed amount is the one that lands hardest on small filers, because the same sum is a larger share of a smaller liability, while a penalty tied to the amount owed scales with size by construction. When a passage gives you the reason alongside the reference, use it to confirm the reference. The harder version of this item type replaces 'the former' with a bare 'it' after a sentence containing three noun phrases, and the technique is the same: write the candidate referents in the margin and substitute each one into the sentence to see which produces a sentence the passage could have meant.
Whose view is this? Stance versus report: Passage: 'Supporters of the levy argue that it will fund three new depots within a decade. That projection rests on traffic volumes holding at 2019 levels, which no forecast in the sector now expects.' Question: what is the author's position? (a) The author supports the levy. (b) The author opposes the levy. (c) The author doubts the depot projection. (d) The author has no view. The correct answer is (c). The stance markers are 'rests on', which frames the projection as dependent on a condition, and 'which no forecast now expects', which tells you the condition is not met. Option (b) is the trap, and it is a scope error dressed as a tone question: undermining one argument made by supporters is not opposition to the policy, and the passage says nothing about the levy's other merits or costs. Reading for stance means reading the author's verbs and qualifiers rather than the content of the views being described - a passage can spend four sentences laying out a position it goes on to dismantle in the fifth.
Procedures: when two steps both fire: Instructions: '1. Record the meter reading. 2. If the reading is lower than last month's, do not submit; raise a query instead. 3. Otherwise, submit the reading and file the photo. 4. If the reading is more than double last month's, submit the reading and flag it for review.' Case A: last month 4,180, this month 9,020. Step 2 does not apply, since 9,020 is higher. Step 3 applies: submit and file the photo. Does step 4 also apply? Double 4,180 is 8,360, and 9,020 is more than that, so yes - submit, file the photo, and flag for review. Steps 3 and 4 are not alternatives; nothing in the list makes them exclusive, and both instruct you to submit, which is consistent. Case B: last month 4,180, this month 4,090. Step 2 fires: do not submit, raise a query. Steps 3 and 4 never come into play, because step 3 begins 'otherwise' and step 4's condition is not met either. The trap in case A is treating a numbered list as a decision tree where exactly one branch executes. Read each step's condition independently unless the procedure explicitly says to stop.
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Synthesizing multiple documents and making a reasoned recommendation.
Build a recommendation from several documents without blurring facts, interpretations, and assumptions. The core method is to frame the decision, organize evidence, test alternatives, and state conditions or gaps.
What you should be able to do after this lesson:
Worked scenario: two service proposals: Proposal A is cheaper and can start now; Proposal B costs more but has stronger reliability evidence. The case does not provide peak-demand data. State the decision criteria first (cost, launch timing, reliability, and demand risk), then compare only supported facts. A defensible recommendation might select a limited A pilot with a reliability threshold and request peak-demand evidence before wider rollout, rather than claiming either proposal is universally best.
Evidence matrix: Use columns for claim, supporting document, strength, contradiction, and gap. When sources conflict, report the conflict and explain how it affects confidence. Repeating every document is summary; connecting evidence to the decision criteria is analysis.
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Evaluating workplace responses against role-relevant principles.
Learn a repeatable way to compare workplace responses: establish the facts, identify duties and risks, respect role boundaries, then choose a proportionate first action. This is educational preparation, not an official scoring guide.
What you should be able to do after this lesson:
Worked scenario: an unverified safety concern: A colleague reports a possible equipment fault while a deadline is approaching. First distinguish the known fact, the report, from the unverified cause. A strong response protects people and affected work, checks the concern through the right channel, tells the relevant lead, and records what was done. Ignoring the report underreacts; shutting down unrelated work or accusing someone before checking the facts overreacts.
Method: facts, duties, risks, response: Write four short notes before ranking options: what is known, who may be affected, which duty or boundary applies, and what safe next step is available now. Prefer an action that addresses the immediate issue and creates useful follow-through. Do not reward an option merely because it sounds decisive.
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