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A Level · Cambridge (CIE) · 9709

Build a count using product and sum rules

Describe what one outcome contains, multiply successive choices and add mutually exclusive cases.

Section 5.2 · Permutations and combinations

Statistics 1 · Permutations and combinations · 5.2
Exam scope and assessment notes

Probability & Statistics 1 (Paper 5, AS & A Level) · Mathematics 9709, 2026–2027, section 5.2. State whether order matters, objects are distinguishable, repetition is permitted and positions or rows are labelled. Count mutually exclusive cases before adding them; account for internal arrangements when using blocks. These lessons include seating in two or more rows. Arrangements in a circle are excluded from this syllabus section. Unless a question says otherwise, people and named roles are distinguishable, and selection is without replacement. Written practice is self-checked; human subject-teacher review remains pending.

Before you begin

  • Multiply and add whole-number counts.
  • List small sets of outcomes systematically.

Work on paper. Try each question before revealing help, and explain why your method works.

Quick readiness check

Which outcome needs one choice from each of two sets?

What you will learn

  • Use the product rule when each outcome requires successive choices.
  • Add disjoint alternatives without counting an outcome twice.
A two-stage choice tree starts with A or B, then digit 1, 2 or 3. Its six distinct leaves are A1, A2, A3, B1, B2, B3. Each code contains one letter and one digit, giving 2 × 3 = 6 outcomes.One letter, then one digitStartAA1A2A3BB1B2B32 × 3 = 6 codesEvery leaf is a complete outcome
A two-stage choice tree starts with A or B, then digit 1, 2 or 3. Its six distinct leaves are A1, A2, A3, B1, B2, B3. Each code contains one letter and one digit, giving 2 × 3 = 6 outcomes.

Define the complete outcome

An outfit consisting of one of three shirts and one of four pairs of trousers has 3 × 4 = 12 possibilities. The word ‘and’ means the outcome contains both choices. A tree makes this concrete: every shirt branch has four trouser branches. Multiplication is a counting rule here; it does not need a probability assumption about independence.

If later choices depend on earlier ones, count the available choices at each stage. A three-character code with first character A or B and last two characters different digits from 1, 2, 3 has 2 × 3 × 2 = 12 possibilities. The second digit has only two choices after the first has been used. Permitting digit repetition instead gives 2 × 3 × 3 = 18.

Add alternatives only when they are disjoint

A journey uses exactly one of six distinct bus services or four distinct train services. These ten named services form disjoint alternatives, so the count is 6 + 4 = 10. It is not 6 × 4: that would describe choosing a bus and a train. An outcome satisfying two overlapping descriptions must not be counted twice.

Before calculating, describe the object being counted and any restrictions. A tree or small list can reveal a missing branch or duplicate. If case sizes differ, count within each case and then add: one universal product may not apply. The same careful outcome definition will later support probability calculations.

Pause and explain

Two letters and three digits form one-letter, one-digit codes. How many?

Put the idea to work

Worked example

List codes whose first character is A or B and whose second character is 1, 2 or 3.

Show the worked solution
  1. For A, the codes are A1, A2 and A3.
  2. For B, they are B1, B2 and B3; these cases do not overlap.
  3. There are 2 × 3 = 6 codes, each with one letter and one digit.

Answer A1, A2, A3, B1, B2, B3: 6 codes.

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From guided practice to a new situation

Write your answer and reasoning first. Use a hint only if you are stuck. The model solution is for self-checking; your written work is not automatically marked.

1 · Guided

How many outfits use one of three shirts and one of four pairs of trousers?

Give me a hint

Each shirt can be paired with every pair of trousers.

Compare my reasoning
  1. Fix one shirt and list its four trouser choices.
  2. Repeat for all three distinct shirts; the groups do not overlap.
  3. Multiply 3 × 4 to obtain 12 complete outfits.

12 outfits.

Look for these in your work

  • Count an outfit containing both items.
  • Explain why each first choice has four continuations.
2 · Independent

A journey uses exactly one of six named bus services or four named train services. All ten services are distinct. How many choices are there?

Give me a hint

The traveller chooses one service, not a bus–train pair.

Compare my reasoning
  1. There are six bus choices and four train choices.
  2. The alternatives are mutually exclusive for this single-service journey.
  3. Add 6 + 4 = 10 choices.

10 service choices.

Look for these in your work

  • Use disjoint alternatives.
  • Explain why multiplying would describe a different journey.
3 · Transfer

A code starts with A or B, followed by two different digits from 1, 2, 3. Count codes, then compare with permitting repeated digits.

Give me a hint

After the first digit, ask how many unused digits remain.

Compare my reasoning
  1. There are two first-letter choices and three first-digit choices.
  2. Without repeated digits, the final choice has size two: 2 × 3 × 2 = 12.
  3. With repeated digits permitted, it has size three: 2 × 3 × 3 = 18.

12 without digit repetition; 18 with repetition.

Look for these in your work

  • Apply the restriction at the correct stage.
  • Keep letter and digit positions distinct.

Common mistakes

  • Define a complete outcome before choosing an operation.
  • Reduce later choices when repetition is forbidden.
  • Do not add overlapping cases without correcting the overlap.
Recall without your notes

What can you explain now?

Does multiplying choice counts require probabilistic independence?

Compare with the explanation

No; count the number of options available at each stage.

The product rule describes a sequence of choices with known branch counts.

After trying it yourself, choose your next review. This is your self-assessment.

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Make it stick

Describe one outcome; multiply successive choices, then add disjoint cases.

If you needed the solution, close it and solve the problem again from a blank page. A correct answer is stronger when you can explain the reason for each step.

Keep learning Mathematics

Course scope and assessment

312 sequenced lessons provide 936 guided, independent and application problems covering the 160 objectives in sections 1.1–1.8, 2.1–2.6, 3.1–3.9, 4.1–4.5, 5.1–5.5 and 6.1–6.5. The map lists all 38 sections across six alternative components; all eight Pure Mathematics 1 sections and all six Pure Mathematics 2 sections, from Paper 2 Algebra through Numerical solution of equations, currently have full teaching sequences. Paper 3 Algebra, section 3.1, links the six shared foundation lessons plus six lessons on partial fractions and rational binomial expansions; Paper 3 logarithms, trigonometry and numerical equations use the shared lessons, with the Paper 1 graph foundation linked into trigonometry. Paper 3 differentiation combines the shared methods with its inverse-tangent derivative lesson. Paper 3 Integration links four shared standard and identity lessons plus seven advanced lessons, covering all six section 3.5 requirements. Eight Vectors lessons cover all six section 3.7 requirements, with 2D and 3D line and solid applications. Nine Differential equations lessons cover all four section 3.8 requirements, including Paper 3 integration techniques, initial conditions, equilibrium solutions and conditional model interpretations. Eleven Complex numbers lessons cover all eight section 3.9 requirements, with seven Argand diagrams and explicit argument and boundary checks. All nine Pure Mathematics 3 sections now have teaching linked to every mapped requirement. Nine Forces and equilibrium lessons cover all seven section 4.1 requirements, with six force diagrams, signed incline-friction intervals, reaction-dependent pull and push limits and separate-body third-law checks. Nine Kinematics lessons cover the four official section 4.2 requirements, with the graph requirement mapped to three relationships. Six motion diagrams support directed quantities, graph slopes and signed areas, Paper 1 time-calculus methods, reversal distances, constant-acceleration stages and shared-clock particle meetings. Six Momentum lessons cover both section 4.3 requirements, including signed system totals, conservation assumptions, outgoing velocities, coalescence, unknown masses and successive impacts. Four diagrams show system momentum through the stipulated events, and supplied kinetic-energy checks distinguish conservation of momentum from changes in kinetic energy. Twelve Newton’s laws lessons cover all four section 4.4 requirements, with vertical support and release stages, rough horizontal and incline motion, inferred parameters, hanging and surface pulley systems, rigid tow-bar thrust and slack-string transitions. Seven force diagrams support the separate-particle balances. Nine Energy, work and power lessons cover all five section 4.5 requirements, with angle-dependent work, kinetic and reference potential energy, signed net-work balances, smooth curved slides, rough and driven slopes, ideal connected-system energy and instantaneous power on hills. Five diagrams distinguish the energy inputs, losses and valid positive-speed force model. All five Mechanics sections now have teaching and staged practice linked to their mapped requirements. Thirteen Representation of data lessons cover all five section 5.1 requirements, including choice and limitations of displays, ordered and back-to-back stems, raw summaries and box plots, unequal-width density histograms, cumulative-frequency interpolation, descriptive moment calculations, grouped estimates, coding, combining two datasets and correcting totals. Six accessible diagrams retain repeated observations and distinguish numerical spread, frequency area and estimated readings. Ten Permutations and combinations lessons cover both official section 5.2 requirements, with selection and ordered roles, repeated objects, adjacency blocks, non-adjacency gaps, leading-zero cases, labelled multi-row seating and mixed restrictions. Six accessible diagrams show outcome trees, ordered versus unordered choices, repeated copies, blocks, gaps and distinct row seats. Eleven Probability lessons cover all four section 5.3 requirements, with equiprobable elementary outcomes, selection-based probabilities, complements and disjoint regions, independence tests, restricted sample spaces, two-way tables, replacement and removal trees, reversed conditions and unknown parameters. Seven accessible diagrams show dice outcomes, random subsets, event regions, a conditioned grid, a count table and two probability trees. Twelve Discrete random variables lessons cover all three official section 5.4 requirements, with complete probability tables, moment and parameter checks, binomial assumptions, exact probabilities and moments, geometric first-success probabilities, tails, expectation and integer reliability thresholds. Six accessible diagrams distinguish outcome grouping, weighted moments, fixed trials, discrete probability bars, waiting conventions and integer tail deadlines. Twelve Normal distribution lessons cover all three official section 5.5 requirements, with continuous-area interpretation, MF19 cumulative tables, full standardisation, tail and interval regions, conditional probabilities, percentiles, one- and two-parameter inference, binomial suitability, continuity corrections and approximate integer thresholds. Seven accessible diagrams show model areas, table reading, aligned scales, intervals, quantiles, matched binomial and normal shapes and whole integer cells. All five Statistics 1 sections now have teaching and staged practice linked to their mapped requirements. Fourteen Poisson distribution lessons cover all five official section 6.1 requirements, with homogeneous event-count assumptions, exposure scaling, exact masses and tails, conditional count events, moments, parameter inference, probability duration targets, disjoint and overlapping windows, rare-count binomial approximations, matched normal approximations and continuity corrections. Nine accessible diagrams show event exposures, unit conversions, discrete bars, complete tail complements, population moments, shared windows, approximate count models and integer cells. Exact checks distinguish approximate and exact close-target decisions. Fourteen Linear combinations lessons cover the six listed results in official section 6.2, with affine moments, signed scales and constant cases, expectation without independence, independent squared variance contributions, fixed and random fees, independent copies versus reused values, affine normal events, independent normal sums and differences, weighted and conditional areas, coefficient inference, exact independent Poisson sums and overlapping exposure limits. Nine accessible diagrams distinguish scale changes, variance contributions, component costs, copy mechanisms, aligned normal scales, summed areas, Poisson count pairs and unsupported distribution claims. Fourteen Continuous random variables lessons cover both official section 6.3 requirements, with density validity and units, support-aware intervals, complements and conditional regions, first and second moments, percentiles by direct density areas, symmetry checks, parameter inference, infinite-support power and exponential integrals, divergent-moment limits and combined applications. Nine accessible diagrams distinguish density height from probability, normalisation, negative-density failures, central and conditional areas, mean versus median, percentile locations and partial infinite tails. Explicit cumulative distribution function knowledge is not required. Eighteen Sampling and estimation lessons cover all eight official section 6.4 requirements, with population and frame definitions, random-number selection, sampling-quality limits, independent sample-mean moments, normal means, CLT conditions, SE scaling, raw, summarised and coded unbiased estimates, known-variance and large-sample mean intervals, repeated-sampling interpretation, precision planning, proportion intervals, sparse-outcome limits and contextual applications. Ten accessible diagrams distinguish selection frames, number rejection, repeated sample means, population versus mean density, CLT models, variance divisors, interval targets, illustrative coverage, integer size checks and proportion margins. Eighteen Hypothesis tests lessons cover all five official section 6.5 requirements, with pre-specified alternatives, inclusive count tails, explicit equal-tail conventions, integer critical regions and actual significance, exposure-aware Poisson tests, continuity-corrected count approximations, normal and large-sample mean tests, confidence-interval connections, error classifications, fixed-region binomial, Poisson and normal error probabilities and sample-size power comparisons. Nine accessible diagrams show the testing procedure, exact count regions, unequal Poisson tails, discrete significance jumps, corrected count boundaries, matching mean intervals and alternative error distributions. All shared lessons are counted once in the course total. All 38 sections now link teaching and practice. Eight earlier overview lessons remain available, but do not count as full section coverage. This is a content-availability measure, not full programme completion or exam readiness. Written practice is self-checked, not officially marked. Check the 2028–2030 syllabus separately if sitting an examination from 2028; this teaching map uses 2026–2027 requirements.

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