statistical skills and data analysis
4 detailed 50-minute lessons with teaching scripts, worked examples, parent guides, and assessment criteria.
Lesson Overview
Total Lessons: 4
Tier: Foundation and Higher
Duration: 50 minutes per lesson (200 minutes total)
Exam Boards: AQA, Edexcel, OCR, Eduqas, CCEA
Tier: Foundation and Higher
Duration: 50 minutes per lesson (200 minutes total)
Exam Boards: AQA, Edexcel, OCR, Eduqas, CCEA
Learning Objectives
- Explain the key ideas of statistical skills and data analysis
- Apply statistical skills and data analysis to exam-style questions
- Develop independent study skills
- Build cross-topic connections
Prerequisites
- Read the revision notes for statistical skills and data analysis first (GCSE Revise summary)
- Key vocab to pre-teach: Common Error, Key Rule, Exam Technique
- Basic skills: reading the summary notes and answering the practice questions there
Materials & Equipment
- Exercise book, coloured pens
- Ruler
- Printed revision notes (link below)
- Internet for videos (see Resources)
Lesson 1: Introduction: statistical skills and data analysis
Duration: 50 minutes
Starter Activity (5 minutes)
Quick Recall
Write down everything you already know about statistical skills and data analysis. Then check against the key terms: Common Error, Key Rule, Exam Technique. Use a mini-whiteboard or paper.
Main Content (35 minutes)
Parent/Teacher Guide:
Before lesson: Read the script below. Pre-teach key vocab: Common Error, Key Rule, Exam Technique.
If stuck: Re-read the revision notes (link above), then break the content into smaller steps.
Extension: See the Stretch & Challenge ideas in Lesson 4.
Before lesson: Read the script below. Pre-teach key vocab: Common Error, Key Rule, Exam Technique.
If stuck: Re-read the revision notes (link above), then break the content into smaller steps.
Extension: See the Stretch & Challenge ideas in Lesson 4.
Teaching Script (35 mins):
Mins 0-5 - Hook: "Today: statistical skills and data analysis. By the end you will be able to answer exam questions on it unaided. It connects to the rest of Geography because the ideas here recur across the spec."
Mins 5-20 - Direct Instruction: Work through the core ideas below one at a time; after each, ask your student to explain it back in their own words.
Mins 20-30 - Guided Practice: Model the worked example together, then let your student attempt the first practice question with guidance.
Mins 30-35 - Independent Practice: 2-3 practice questions from Lesson 3 below, with immediate feedback.
Mins 0-5 - Hook: "Today: statistical skills and data analysis. By the end you will be able to answer exam questions on it unaided. It connects to the rest of Geography because the ideas here recur across the spec."
Mins 5-20 - Direct Instruction: Work through the core ideas below one at a time; after each, ask your student to explain it back in their own words.
Mins 20-30 - Guided Practice: Model the worked example together, then let your student attempt the first practice question with guidance.
Mins 30-35 - Independent Practice: 2-3 practice questions from Lesson 3 below, with immediate feedback.
First Look
Start with the revision notes summary, then attempt: Calculate the mean, median, mode, and range for the following data: 12, 15, 18, 18, 22, 25, 30, 35
Plenary (5 minutes)
Check Out
Your student states one thing they learned and one question they still have about statistical skills and data analysis.
Lesson 2: Core Concepts: statistical skills and data analysis
Duration: 50 minutes
Starter Activity (5 minutes)
Review Previous Lesson
Quick recap: write 3 key points from Lesson 1 on statistical skills and data analysis. Check them against the notes below.
Main Content (35 minutes)
Definition: Measures of central tendency identify the "typical" or "middle" value in a dataset. The three main measures are the mean, median, and mode.
Definition: The IQR measures the spread of the middle 50% of data, removing the influence of the lowest 25% and highest 25% of values. It is more robust than the range because it ignores extreme values.
Definition: A box and whisker plot visually displays the median, quartiles, and range of a dataset. The "box" shows the IQR (Q1 to Q3), the line inside the box is the median, and the "whiskers" extend to the minimum and maximum values (or to 1.5 × IQR from the box, with outliers shown as individual points).
Common Error: Using the new value as the denominator instead of the original. The percentage change must always be calculated relative to the ORIGINAL (starting) value, not the final value. Also, be careful to distinguish between percentage change and percentage point change (e.g. unemployment rising from 5% to 7% is a 2 percentage point increase, but a 40% increase relative to the original 5%).
Definition: A percentile is the value below which a given percentage of data falls. The 25th percentile (P25) is the value below which 25% of data lies - this is the same as Q1. The 50th percentile (P50) is the median. The 75th percentile (P75) = Q3.
Definition: Bivariate data involves two variables that are measured for each observation. The purpose is to investigate whether a relationship (correlation) exists between the two variables. In geography, bivariate data is commonly displayed on scatter graphs.
| Term | Meaning | Example |
|---|---|---|
| Mean | When data has no extreme outliers; for further calculations | Average rainfall across a year; mean GNI per head for a region |
| Median | When data has outliers that would distort the mean | Typical house price in an area (a few mansions would distort the mean) |
| Mode | For categorical data; finding the most common value | Most common rock type; most frequent wind direction |
| Where | Within the data range | Outside the data range |
| Reliability | Generally reliable | Less reliable - trend may change |
| Assumption | Trend between known points continues | Trend continues beyond known data |
| Risk | Low - supported by surrounding data | High - no data to verify the estimate |
| Calculate the mean/median/mode | Show your working; arrange data in order for median; identify the most frequent for mode |
Practice (10 minutes)
Q: Calculate the mean, median, mode, and range for the following data: 12, 15, 18, 18, 22, 25, 30, 35
Answer: Mean = (12+15+18+18+22+25+30+35) ÷ 8 = 175 ÷ 8 = 21.875 ≈ 21.9. Median = (18+22) ÷ 2 = 20 (middle two of 8 values). Mode = 18 (appears twice, all others appear once). Range = 35 − 12 = 23.
Plenary (5 minutes)
Explain Back
Your student teaches the key points back to you without looking. Fill any gaps immediately.
Lesson 3: Application: statistical skills and data analysis
Duration: 50 minutes
Starter Activity (5 minutes)
Quick Recall
Recall the key terms: Common Error, Key Rule, Exam Technique. Define each in one sentence.
Main Content (35 minutes)
Parent/Teacher Guide: Let your student attempt each question alone first, then compare with the model answer. Award method marks for correct working even if the final answer is wrong.
Q1: Calculate the mean, median, mode, and range for the following data: 12, 15, 18, 18, 22, 25, 30, 35
Answer: Mean = (12+15+18+18+22+25+30+35) ÷ 8 = 175 ÷ 8 = 21.875 ≈ 21.9. Median = (18+22) ÷ 2 = 20 (middle two of 8 values). Mode = 18 (appears twice, all others appear once). Range = 35 − 12 = 23.
Q2: A country's exports were $4.2 billion in 2015 and $6.3 billion in 2025. Calculate the percentage change.
Answer: Percentage change = ((6.3 − 4.2) ÷ 4.2) × 100 = (2.1 ÷ 4.2) × 100 = 0.5 × 100 = 50% increase.
Q3: Explain the difference between the range and the interquartile range. Why is the IQR often preferred?
Answer: The range is the difference between the highest and lowest values, using only two data points. The IQR is the difference between Q3 and Q1, measuring the spread of the middle 50% of data. The IQR is preferred because it ignores the most extreme values (the top 25% and bottom 25%) that may be outliers, giving a better representation of typical variation. For example, if one house in an area costs £5 million while others are £150,000-£300,000, the range would be huge but the IQR would still reflect typical variation.
Q4: What is the difference between interpolation and extrapolation? Which is more reliable and why?
Answer: Interpolation estimates a value within the existing data range by reading off the line of best fit. Extrapolation estimates a value beyond the data range by extending the line of best fit. Interpolation is more reliable because it is supported by observed data on either side of the estimate. Extrapolation is less reliable because it assumes the trend continues in the same way beyond the data, which may not be true (e.g. a population growth trend may flatten off).
Q5: Explain what is meant by "correlation does not prove causation" with a geographical example.
Answer: "Correlation does not prove causation" means that just because two variables are statistically related, it does not mean one causes the other. There may be a third underlying factor affecting both. For example, there is a positive correlation between a country's GNI per head and life expectancy, but higher GNI does not directly cause longer life - instead, higher GNI enables better healthcare, nutrition, and sanitation, which cause improved health. The correlation identifies a relationship, but understanding the mechanism requires geographical knowledge.
Q6: Using the following data, calculate Q1, Q3, and the IQR: 5, 8, 11, 14, 17, 20, 23, 26, 29, 32, 50
Answer: 11 values arranged: 5, 8, 11, 14, 17, 20, 23, 26, 29, 32, 50. Median = 20 (6th value). Lower half: 5, 8, 11, 14, 17 → Q1 = 11. Upper half: 23, 26, 29, 32, 50 → Q3 = 29. IQR = Q3 − Q1 = 29 − 11 = 18. Note: the range is 50 − 5 = 45, but the IQR of 18 better represents the typical spread because the outlier of 50 inflates the range.
Plenary (5 minutes)
Error Review
Review any questions answered incorrectly. Identify whether the error was knowledge, method, or reading the question.
Lesson 4: Exam Practice: statistical skills and data analysis
Duration: 50 minutes
Starter Activity (5 minutes)
Command Words
Review what these command words require: state (one point), describe (say what happens), explain (say why), compare (both sides), evaluate (judgement).
Main Content (35 minutes)
Exam-Style Question
Attempt a past-paper style question on statistical skills and data analysis from the exam board past paper finder (see Resources), then mark it against the scheme.
Exam Tips: Always show your working in statistical calculations - marks are given for method as well as answer | When calculating the median, always arrange data in order first | For percentage change, always divide by the ORIGINAL value (the starting value) | When comparing datasets, discuss central tendency AND spread | Remember: correlation ≠ causation - always consider alternative explanations | For interpolation/extrapolation questions, state which one you are doing and comment on reliability | Practise calculating IQR - it appears on both fieldwork and skills questions | Use precise statistical language: "positively correlated", "significant outlier", "central tendency" | Know the difference betw
Common Errors: Watch Out! Students often write vague answers without specific geographical evidence. Wrong: Writing generalised statements like 'it causes problems' Correct: Using specific data and named examples, e.g. 'the 2010 Haiti earthquake killed over 200,000 people due to poor building quality' Students often confuse causes and effects. Wrong: Mixing up what caused the event with what resulted from it Correct: Clearly separate causes (why it happened) from effects (what happened as a result) Students often describe rather than evaluate. Wrong: Listing strategies without assessing their effectiveness Correct: Weighing up strengths and weaknesses of each approach and reaching a supported judgement
Stretch & Challenge (Grade 8-9):
- Synoptic links: explain how statistical skills and data analysis connects to another Geography topic you have studied
- Real-world: research one real-world use or example of statistical skills and data analysis
- Critical: "What are the limitations of the models used in statistical skills and data analysis?"
Plenary (5 minutes)
Assessment Criteria
- Got it: Confident explanation + correct worked examples
- Getting there: Main points OK, needs support with detail
- Not yet: Confused on key concepts - re-run Lesson 2
✅ Success Criteria
You are ready for statistical skills and data analysis - GCSE Geography Lessons when you can:
- [ ] Define the key terms in your own words
- [ ] Explain the core concept with an example
- [ ] Apply the method to an unfamiliar context
- [ ] Score 80%+ on past-paper questions
Tick each item honestly — if any box is unticked, re-read the relevant section above.
📝 Mark Scheme Guidance
How examiners mark statistical skills and data analysis - GCSE Geography Lessons: Read the command word carefully — State = one word/phrase (1 mark), Describe = pattern or detail (2 marks), Explain = reason with "because" (2–3 marks), Compare = similarities AND differences, Evaluate = both sides + conclusion. Method marks are awarded for correct working even if the final answer is wrong. Always show your working — unexplained answers score zero for AO2.
Success on statistical skills and data analysis - GCSE Geography Lessons questions: Underline the command word, count the marks (one mark per point expected), and answer in the format the command word demands.
Board Notes & Revision Links
This lesson is examined by: AQA 8035 | Edexcel 1GB0 | OCR J384 | Eduqas A/B | CCEA units. Tier and paper details vary - always check your board's current specification.
Follow-up revision: G48-statistical-skills on GCSE Revise (notes, flashcards and auto-graded target tests for this exact topic).
Homework & Consolidation
- Consolidation: Re-answer any Lesson 3 practice questions answered incorrectly (20 mins)
- Retrieval: Write flashcards for the key terms: Common Error, Key Rule, Exam Technique (10 mins)
- Exam practice: One past-paper question on statistical skills and data analysis from the board websites (15 mins)
- Extension: Explain statistical skills and data analysis to someone else in your own words (10 mins)