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BUSS5221 WEEK12
Data-based argument







主讲人：Amy










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1.Weaving Numbers into narratives
• Data alone do not answer questions or clarify problems on their own. At their best they help us
understand what is happening
• They have limited functions in proving how and why things happen.

2.Narrative-Numbers nexus:
• Principles concerning communication with and through data
Listening to data carefully and working sensitively with it to help you devise robust arguments that are
compelling as well as robust
• Principles concerning critical assessment of data based arguments
Positivism vs constructivism

3.Communication goes both way
• As in any aspect of life, communicating with data is complex.
• It is important that you develop the skills of being able to overcome biases and prejudices so that you
can absorb what the data are trying to convey.
• Presenting and reconfiguring data in different ways is one way to help with this process.

3.1 Listening to the data







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3.2 Data needs to be communicating





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3.3 Visualisation as both 'listening' and 'communicating'

Snow's 1854 Cholera Map of London


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Minard and his account of Napoleon's army's fate




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4. Assessing the quality of analysis
Positivist/naturalist tradition Constructivist tradition
• Facts are assumed to be phenomena that are
discovered
• Draws on the model of natural sciences
• Quality of an argument is assessed by how
well facts as discovered support or disprove
theories
• Facts are not assumed to be already existing
and waiting for discovery but that as our
understanding of things has emerged and
evolved, our ability to make sense of the
world has improved
• Need to understand under what conditions
facts are constructed

Contributions from the positivist tradition: minimum technical requirements
• Reliability
• Validity
• Accuracy
• Reproducibility

Contributions from the constructivist tradition: the importance of context:
• Question the source of the information
• Beware of unfair comparisons
• If it seems too good or too bad to be true
• Think in orders of magnitude
• Avoid confirmation bias
• Consider multiple hypothesis