Free course

Teaching and learning statistics

This course focuses on teaching and learning statistics relevant to learners aged 8 to 14 years and is aimed at school teachers, teaching assistants, parents and anyone interested in supporting a young learner in understanding statistics.

Course learning outcomes

After studying this course, you should be able to:

TBC

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First Published: 03/04/2025

Updated: 03/04/2025

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Course content

- Introduction and guidance
3 pages contain editor comments.
- Current section:
  Introduction and guidance
  Welcome to this free badged course, Teaching and learning statistics.The course follows the way teachers approach a statistical investigation in the classroom, working through the full statistical process from start to end. The best way to learn mathematics and statistics is to do it! Statistics is used in many curriculum subjects, for example geography, PE, business and many others; so this course is not solely aimed at teachers of mathematics. Whilst core methods are covered and explained,...
  (1)
- What is a badged course?
  (1)
- How to get a badge
- Acknowledgements
  (1)
- Week1Week 1 The data handling cycle
3 pages contain author comments.
11 pages contain editor comments.
- Current section:
  Introduction
  A cycle is a series of events in order that are repeated for example the annual cycle of the seasons, winter, spring, summer, autumn. Author - I’m wondering how much the short animation of a cyclist actually adds to the page. Is there anything else you could say here to illustrate the link?The data handling cycle is no different; it is a series of events performed in order that aim to use data to answer a question.
  (1)
- Learning outcomes
- 1 Statistics
  (1)
- 2 The data handling cycle
  (1)
  - 2.1 The statistical process
    (4) (1)
  - 2.2 The cycle
- 3 Planning
  (1)
  - 3.1 Pose the question
    (2)
  - 3.2 Personal data
    (1)
  - 3.3 Appropriate questions
    (1) (2)
  - 3.4 Projects for the classroom
  - 3.5 Data sources
- 4 This week’s quiz
  (1)
- 5 Summary of Week 1
- References
- Acknowledgements
  (1)
- Week2Week 2 Collecting and organising data
5 pages contain editor comments.
- Current section:
  Introduction
  In Week 1 you were introduced to the data handling cycle, also known as the PCAI cycle, and some of the elements that should be considered at the first stage of a statistical investigation, the ‘Pose’ stage. This week you are going to move onto the second stage, ‘Collect’.When data is collected from a population, it is rare that a data value from every possible person or specimen has been collected. Before starting this week, perhaps you could reflect why this might be; you can then return ...
- Learning outcomes
- 1 Sampling
- 2 Types of data
  - 2.1 An important note about bar charts
- 3 Organising data
- 4 This week’s quiz
- 5 Summary of Week 2
- 6 Further reading/listening/watching
- References
- Acknowledgements
  (1)
- Week3Week 3 Measures of location and dispersion
1 page contains author comments.
6 pages contain editor comments.
- Current section:
  Introduction
  Every day we are surrounded by information, often in the form of numbers. Sometimes these figures can seem trivial, such as the claim that Britain drinks 100 million cups of tea per day on average, or that your fingernails grow 3.5 millimetres per month. However, these numbers often reflect – and possibly affect – your daily life.Perhaps you have already looked up today’s weather using an app or logged your daily step count using a fitness tracker. Getting data from a measurement or an ...
- Learning outcomes
- 1 Averages
  (2)
- 2 Dispersion - why measure spread?
- 3 Doing and undoing
  (1)
- 4 This week’s quiz
- 5 Summary of Week 3
  (1)
- References
- Acknowledgements
- Week4Week 4 Presentation of discrete data
11 pages contain editor comments.
- Current section:
  Introduction
  In Week 1 we said, ‘The early decisions about your question and your data will impact on the analysis and interpretation.’ Then in Week 2 you went on to look at the different types of data such as qualitative and quantitative data, breaking quantitative data down into discrete or continuous data types. The way data is presented is dependent on the type of data and this is a key point often missed when teaching statistical graphs to younger students. Whilst it is not necessary for younger ...
- Learning outcomes
- 1. Pictograms
  (6)
- 1.1 Appropriate symbols
  (3)
- 2. Bar charts
  (1)
- 2.1 Block diagrams
  (4)
- 2.2 Dot plots
  (1)
- 2.3 Vertical line charts
  (1)
- 2.4 Formatting bar charts
  (7)
- 2.5 Vertical or horizontal bar charts?
  (4)
- 3. Stem-and-leaf diagrams or stemplots
  (5)
- 4. Pie charts
  (7)
- 4.1 Connecting bar charts and pie charts
  (6)
- 5. Context of the data
- Further reading/listening/playing!
- Acknowledgements
- Week5Week 5 Presentation of continuous data
10 pages contain editor comments.
- Current section:
  Introduction
  In Week 2, we discussed the key difference between discrete and continuous data, which is that a discrete quantity has an ‘exact’ value, and a continuous quantity can never be ‘exact’ since you could always measure it more or less precisely. Given that much of the information we collect is measured, and therefore continuous, it is important for learners to be confident in working with this data. In Week 4 you looked at pictorial representations best suited to discrete data sets; here you ...
- Learning outcomes
- 1. Time series and line graphs
- 1.1 Time series
- 1.2 Plotting and reading line graphs
  (2)
- 2.1 Drawing box plots
  (13)
- 2.2 Interpreting box plots
  (4)
- 2.3 Dispersion on box plots
  (1)
- 2.4 Very extreme data values
  (1)
- 3. Histograms
  (3)
- 3.1 Histograms with equal width bars
  (2)
- 3.2 Frequency polygons
  (2)
- 3.3 Histograms with unequal width bars
  (13)
- 5. The week's quiz
  (1)
- 6. Summary of week 5
- 7. Further reading
- Acknowledgements
- Week6Week 6 Comparing data and interpreting results
11 pages contain editor comments.
- Current section:
  Introduction
  Over the last two weeks, you have looked at ways to present individual datasets. Sticking with the analysing and interpreting sections of the data handling cycle, this week you will look at comparing datasets and studying relationships. Comparing data helps us make sense of it, identify patterns, make judgements and draw conclusions. However, we do need to be wary of making statements based on connections that may be just coincidence!This OpenLearn course is an adapted extract from the Open ...
  (1)
- Learning outcomes
- 1.Composite bar charts
  (7)
  - 1.1 Dual bar charts
    (2)
  - 1.2 Back-to-back bar charts
    (4)
  - 1.3 Population pyramids
    (9)
- 2. Comparative box plots
  (1)
- 3. Bivariate data
  (2)
  - 3.1 Scatter graphs
    (7)
  - 3.2 Correlation
    (12)
  - 3.3 Prediction
    (4)
  - 3.4 Correlation and causation
    (7)
- 4. This week’s quiz
- 5. Summary of Week 6
- 6. Further reading
- 7. Acknowledgements
- References
- Acknowledgements

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