Accessibility statement

Lies, Darn Lies, & Statistics - for Historians - HIS00107M

« Back to module search

  • Department: History
  • Module co-ordinator: Dr. David Clayton
  • Credit value: 10 credits
  • Credit level: M
  • Academic year of delivery: 2021-22

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 cycle
A Spring Term 2021-22

Module aims

The module aims to:

  • introduce students to quantitative methods
  • provide a foundation for understanding analytical statistics in historical studies
  • develop familiarity with statistical software packages
  • build an understanding of some of the theoretical issues in quantitative analysis
  • facilitate students’ use of quantitative methods in their own research

Module learning outcomes

At the end of this module students will be able to:

  • understand the way in which historians have used statistical methods in research
  • recognize basic statistical methods of measurement
  • use basic software techniques to analyse data
  • order data and use it to measure historical phenomena and causation
  • present ordered data in an intelligible way

Module content

Students will attend a 2-hour seminar, two workshops and a mini-conference in the spring term.

Week 1: Briefing (1 hour)

Week 2: Context/theory seminar: Statistics 101 (2 hours)

Week 3: Practical workshop I: Visualising (4 hours)

Week 4: Practical workshop II: Working with a data set (4 hours)

Weeks 5-8: Independent project work

Week 8: Project Mini-Conference (3 hours)

Assessment

Task Length % of module mark
University - project
Project Portfolio
N/A 100

Special assessment rules

None

Additional assessment information

Students will submit a project portfolio in week 10 of the spring term for summative assessment, comprising of a datasheet and a 1000-word reflective essay.

Using raw historical data sources derived from the core readings (see above), students will manipulate a historical data series, reordering numbers, presenting frequency distributions and times series. Students will also write a reflective essay (no more than 1000 words) justifying their methods and describing their findings and explaining their significance for solving historical problems.

They will use text to justify methods, describe findings, and explain their significance for solving historical problems. It is recommended that this work is grounded on supplementary reading using the three core texts. These are all very different and provide appendices and exercises to further knowledge and master techniques, citing examples of exemplary historical articles.

Prior to that in week 8, students will make a short presentation to the group at the mini-conference about their chosen project, the research they have undertaken, and their likely direction for the reflective essay.

Reassessment

Task Length % of module mark
University - project
Project Portfolio
N/A 100

Module feedback

Following their formative assessment task, students will receive constructive verbal feedback from the module convenor and their peers during the mini-conference, which they can then take forward into the completion of their final project portfolio.

For their summative assessment task, students will receive written feedback within four working weeks of the submission deadline, after which the convenor will be available during student hours for follow-up guidance if necessary. For more information, see the Statement of Assessment

Indicative reading

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

Hudson, Pat and Mina Ishizu. History by Numbers: An Introduction to Quantitative Approaches. London: Bloomsbury, 2016.

Wheelan, Charles. Naked Statistics: Stripping the Dread from the Data. New York: WW Norton & Company, 2013.



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.