ACCA 2027 · Foundations
ACCA F3 Decision Making with Data
F3 Decision Making with Data is a Foundations-level exam in ACCA’s 2027 qualification. It is an entirely new paper with no current-syllabus equivalent. It is a CBE, 2 hours, 100 marks exam with a 50% pass mark, and the first exams run from July 2027.
Key facts
- Level
- Foundations
- Format
- CBE · 2 hours · 100 marks
- Pass mark
- 50%
- Replaces
- None (entirely new paper)
- First exams
- July 2027 (on-demand CBE)
- Syllabus
- 6 sections (A, B, C, D, E, F), 11 subsections, 38 learning outcomes
- Employability module at this level
- Enterprise and Entrepreneurship
Overview
What F3 covers
F3 Decision Making with Data is an entirely new paper with no direct ACCA predecessor. It introduces data literacy, basic analytics, and data-informed decision making at the entry level. Students learn to interpret data presentations, understand basic statistical concepts, use spreadsheets for analysis, and apply data to business decisions.
Exam format
| Part | Detail |
|---|---|
| Section A | 45 × 2-mark OTs (90 marks) |
| Section B | 1 × 10-mark MTQ |
| Overall | 2 hours |
| Overall | 100 marks |
Key changes and notes
- Entirely new paper — no direct ACCA predecessor
- Introduces data literacy and analytics at entry level
- Covers spreadsheet skills, basic statistics, data visualisation
- Includes data ethics and decision-making frameworks
Syllabus comparison
Where F3’s content comes from
12 changes, from whole sections removed to topics added or raised in intellectual level.
| Change | Area or topic | Detail |
|---|---|---|
| ORIGIN | A1 Sampling methods | MA/FMA B1 — identical content (sampling methods). MA/FMA B3 covered data types (categorical/numerical) which is now in F3 A1. |
| ORIGIN | A2 Summarising and analysing data | MA/FMA B3 (partial) — mean/mode/median and dispersion measures for ungrouped data retained. Removed from F3: mean for grouped data; expected values; descriptive vs inferential analysis; normal distribution properties and graph interpretation. |
| ORIGIN | A3 Linear functions (including high-low method) | MA/FMA B2a (structure of linear functions), B2b (high-low method), B2d (scatter diagrams). MA/FMA B2b also covered advantages/disadvantages of high-low — retained in F3 A3d. |
| ORIGIN | A4 Linear regression | MA/FMA B2e (correlation, coefficient of determination, regression analysis), B2f (use regression for forecasts), B2h (advantages/disadvantages of linear regression). Note: MA/FMA B2g (adjust historical/forecast data for price movements) is not in F3 A4. |
| ORIGIN | A5 Time series analysis | MA/FMA B2i–B2m — all time series content retained: components, moving averages, trend calculation, additive and multiplicative forecasting, advantages/disadvantages. MA/FMA B2n–B2p (index numbers — Laspeyre/Paasche; product life cycle) are NOT in F3. |
| ORIGIN | A6 Big data | MA/FMA B3a–B3c — 5Vs, three types, main uses. MA/FMA B3d–B3j (data types, descriptive/inferential, mean/median, variance, expected values, normal distribution) are split: B3a–c go to F3 A6; B3d–j go partly to F3 A2 (measures of dispersion) and the rest are removed. |
| ORIGIN | B1 Relevant costing principles | NEW at Foundations level. Previously only in PM (Applied Skills — F5). Simplified: asset replacement decisions and opportunity cost excluded from F3 scope. Appears also in K2 Section E1a at Knowledge level. |
| ORIGIN | B2 Limiting factor analysis | NEW at Foundations level. Previously only in PM (Applied Skills — F5). Single resource constraint only (no linear programming/multiple constraints). Appears also in K2 Section E1b at Knowledge level (K2 adds shadow price concept). |
| ORIGIN | B3 CVP analysis | NEW at Foundations level. Previously only in PM (Applied Skills — F5). Appears also in K2 Section E1c at Knowledge level (K2 adds multi-product CVP and P/V chart preparation). F3 restricts to single product; K2 adds multi-product. |
| ORIGIN | B4 Inventory management techniques | MA/FMA C1a (partial) — ordering/holding costs, EOQ, reorder levels retained. NOT in F3: LIFO valuation; gradual replenishment EOQ; buffer inventory calculations; interpret material inventory account entries. Adds material input requirements with wastage allowance as a new explicit outcome. |
| ORIGIN | C1 Compounding and discounting | MA/FMA D4d (simple vs compound interest; nominal vs effective interest rates) and D4e (compounding and discounting). Retained fully in F3 C1. |
| ORIGIN | C2 Investment appraisal techniques | MA/FMA D4f–D4k (cash flow vs profit; NPV and IRR; annuity and perpetuity; calculate NPV/IRR/payback; interpret results). F3 adds ROCE explicitly as a named method. Annuities and perpetuities are NOT in F3 C2 (they are in K2 Section E2 at Knowledge level). MA/FMA D4a–D4c (importance of investment planning; asset vs expense; steps in asset expenditure budget) are also not in F3. |
Syllabus
F3 syllabus breakdown
Every section, subsection and learning outcome in the F3 Decision Making with Data syllabus.
AData and Information in Business
A1The role of data and information
- Distinguish between data, information and knowledge
- Identify sources of data: internal and external, primary and secondary
- Understand the value of data as an organisational asset
- Identify the characteristics of useful information: accurate, complete, timely, relevant, understandable
BData Collection and Organisation
B1Data types and collection
- Identify data types: qualitative, quantitative, discrete, continuous
- Understand sampling methods: random, systematic, stratified, cluster, quota
- Understand the importance of data quality and data cleansing
B2Data organisation
- Organise data using tables, databases and spreadsheets
- Sort, filter and group data
- Understand data structures: flat files, relational databases
CSpreadsheet Skills for Analysis
C1Spreadsheet functions
- Use key spreadsheet functions: SUM, AVERAGE, COUNT, MAX, MIN, IF, VLOOKUP
- Create and format charts and graphs
- Use pivot tables for data summarisation
- Apply conditional formatting
DStatistical Concepts and Data Analysis
D1Descriptive statistics
- Calculate and interpret measures of central tendency: mean, median, mode
- Calculate and interpret measures of dispersion: range, variance, standard deviation
- Understand frequency distributions and histograms
D2Correlation and trends
- Understand correlation: positive, negative, no correlation
- Interpret scatter diagrams and correlation coefficients
- Identify trends in time series data
- Calculate moving averages
D3Index numbers and probability
- Calculate and interpret simple index numbers
- Understand basic probability concepts
- Apply expected values to decision making
EPresenting and Interpreting Data
E1Data visualisation
- Select appropriate charts: bar, line, pie, scatter, histograms
- Interpret charts and graphs
- Identify misleading data presentations
- Present data clearly and accurately
E2Report writing
- Structure a short data-based report
- Draw conclusions from data analysis
- Make recommendations based on data
FData-Informed Decision Making
F1Decision making frameworks
- Apply break-even analysis
- Understand cost-volume-profit (CVP) relationships
- Apply relevant costing principles to short-term decisions
- Use expected values in decision making under uncertainty
F2Data ethics and limitations
- Understand data protection and privacy principles
- Recognise limitations of data in decision making
- Understand bias in data collection and analysis
ACCA F3: frequently asked questions
What is the ACCA F3 exam format?
F3 Decision Making with Data is a CBE, 2 hours, 100 marks exam. Structure: Section A: 45 × 2-mark OTs (90 marks) · Section B: 1 × 10-mark MTQ · 2 hours · 100 marks. Pass mark: 50%.
When is the first ACCA F3 exam?
July 2027. New Foundations and Knowledge exams are on-demand computer-based exams from July 2027. The current syllabus’s last sitting is June 2027.
Which current ACCA paper does F3 replace?
None. F3 Decision Making with Data is an entirely new paper in the 2027 qualification.
What changed in F3 compared with the current syllabus?
Entirely new paper — no direct ACCA predecessor. Introduces data literacy and analytics at entry level. Covers spreadsheet skills, basic statistics, data visualisation. Includes data ethics and decision-making frameworks.
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