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Lies, Darn Lies, & Statistics - for Historians - HIS00142M

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  • Department: History
  • Module co-ordinator: Prof. David Clayton
  • Credit value: 20 credits
  • Credit level: M
  • Academic year of delivery: 2023-24

Module summary

Mark Twain attributed the phrase ‘Lies, Darn Lies, and Statistics’ to the British Prime Minister Benjamin Disraeli. He was wrong – Disraeli did not utter it. But he was also right: the devious can use numbers to support weak arguments. This is because thinking numerically does not come naturally to us. We instinctively prefer Tom Sawyer, Twain’s invented narrator, to tell us a story. Our number-blindness gives propagandists using statistics a major head start. Aware of this universal trait, and conscious that most BA History courses avoid teaching formal quantitative history, this module provides a basic introduction into how historians use quantitative methods. It focuses on two simple problems: how to classify, and order data and how to use it to describe changes across time.

Students will learn practical skills (how to use excel; and how to present using tables and charts) and debate philosophical issues (turning numbers into evidence). You require no prior knowledge of statistics: if you can count, and want to collect numbers from the past, sign up.

Module will run

Occurrence Teaching period
A Semester 2 2023-24

Module aims

The aims of this module are to:

  • Develop skills of source analysis and interpretation
  • Assess a range of source material and relevant secondary works; and
  • Develop students’ powers of evidence-based historical argument, both orally and in writing.

Module learning outcomes

Students who complete this module successfully will:

  • Demonstrate a knowledge of a specialist historiographical literature;
  • Present findings in an analytical framework derived from a specialist field;
  • Solve a well-defined historiographical problem using insights drawn from secondary and, where appropriate, primary sources.
  • Set out written findings using a professional scholarly apparatus.

Module content

Students will attend a 1-hour briefing in week 1. Students will then attend a 2-hour seminar in weeks 2-4, 6-8 and 10-11. Weeks 5 & 9 are Reading and Writing (RAW) weeks during which there are no seminars, and during which students research and write a formative assignment, consulting with the module tutor. Students prepare for eight seminars in all.

Seminar topics are subject to variation, but are likely to include the following:

  1. Putting observations into proportion
  2. Summarising lots of numbers
  3. What’s normal/abnormal?
  4. Cause and correlation
  5. Reading regressions
  6. Excel workshop I
  7. Excel workshop II
  8. Troubleshooting projects


Assessment

Task Length % of module mark
Essay/coursework
Long Essay		 N/A 100

Special assessment rules

None

Additional assessment information

Students will present a draft spreadsheet and table/chart for their formative assignment, and, for their summative work, students will submit a 4000-word essay which will include a visualisation of a qualitative data source.

Reassessment

None

Module feedback

Students will typically receive written feedback on their formative spreadsheet-assignment within 10 working days of submission.

Work will be returned to students in their seminars and may be supplemented by the tutor giving some oral feedback to the whole group. All students are encouraged, if they wish, to discuss the feedback on their formative assignment during their tutor’s student hours—especially during week 11, before, that is, they finalise their plans for the Summative Essay.

For more information, see the Statement on Feedback.

For the summative assessment task, students will receive their provisional mark and written feedback within 25 working days of the submission deadline. The tutor will then be available during student hours for follow-up guidance if required. For more information, see the Statement of Assessment.

Indicative reading

For reading during the module, please refer to the module VLE site. Before the course starts, we encourage you to look at the following items of preliminary reading:

  • Pat Hudson, and Mina Ishizu. History by Numbers: An Introduction to Quantitative Approaches (London: Bloomsbury, 2016.)
  • David Spiegelhalter, The Art of Statistics. Learning from Data (London. Pelican, 2019.)


The information on this page is indicative of the module that is currently on offer. The University is constantly exploring ways to enhance and improve its degree programmes and therefore reserves the right to make variations to the content and method of delivery of modules, and to discontinue modules, if such action is reasonably considered to be necessary by the University. Where appropriate, the University will notify and consult with affected students in advance about any changes that are required in line with the University's policy on the Approval of Modifications to Existing Taught Programmes of Study.