Overview

Claims such as “AI raises productivity by 30%” compress many hidden decisions into a single number. This course teaches learners how to unpack those decisions.

Each investigation begins with population, comparison group, time period, measurement and source. Learners then ask whether the reported number is descriptive, experimental, model-based or extrapolated.

The course is designed for practical use in research, policy, journalism and business analysis where quantitative claims often travel farther than their original evidence.

Learning objectives

By the end of the course, learners should be able to:

• identify the population, denominator, benchmark and time period behind a quantitative claim;
• trace reported statistics to their original source where possible;
• distinguish experimental, observational, survey and model-based evidence;
• recognize selection, survivorship and measurement problems;
• test whether a headline interpretation follows from the underlying data;
• write a concise verification note with appropriate caveats.

Intended participants and prerequisites

Course structure

The course is organised into six units. Each unit combines concise explanation, worked examples and short analytical exercises.

Unit	Subject	Principal questions
1	Define the number	What exactly is measured, over which population and period?
2	Find the source	Where did the statistic originate, and how far has it travelled?
3	Understand the design	Was the result experimental, observational, surveyed or modelled?
4	Check the comparison	Compared with what baseline, control or counterfactual?
5	Test robustness	Would the conclusion change under reasonable alternative definitions?
6	Report the finding	How should uncertainty and limitations be communicated clearly?

Teaching and assessment

Teaching is delivered through concise lectures, technical notes, worked examples and guided exercises. AI-supported interaction may be used to explain concepts, generate additional practice and challenge a learner's reasoning, but assessment remains based on the learner's demonstrated understanding.

Component	Purpose	Weight
Short exercises	Check understanding of the principal concepts	40%
Applied investigation	Use the course framework on a new problem or dataset	30%
Final assessment	Integrated reasoning across the course	30%

A certificate of completion is issued only when the required assessments have been completed at the stated standard. Participation alone does not constitute completion.

Faculty

Materials and technical requirements

Learning environment

Learners need a current web browser and access to the Gordon learning platform. Where a course uses code, spreadsheets, datasets or external readings, the relevant requirements are identified inside the unit.

Course materials

Required readings, datasets and practice materials are identified within each unit. Public courses are designed so that required core materials can be accessed without a separate tuition charge.