Short Notes on Thinking with Data

home · about · subscribe

Introduction

• Arguments are how we convince ourselves that something is true.
• Argument theory provides a useful set of mental models for understanding arguments.
• Data work is held together by arguments.

Data

• Data are just (sadly) observations.
• What we want is knowledge and representation, maybe even understanding and insight.
• By doing so, arguments move from what is unknown/not agreed upon yet.

Arguments are how we turn observations into knowledge.

Arguments

• Claim => Audience does not believe it(yet).
• Prior Knowledge => Things you/your audience believes already before the case is made.
• Evidence => Where data enters into an argument. It becomes part of the argument, not just “data” anymore. It gains context in that sense.
• Justification => Reasoning why evidence should cause vs to believe the claim.
• Rebuttal => Any of the reasons why the justification might not hold in this particular case.

Patterns of Arguments

Template for Claims

• Categories of Dispute
• Causal Analysis

Causal Analysis

• Large set of patterns are necessary.
• Goal is to claim to have reasonably accounted for alternative explanations.
• People will interpret our work causally, we should try our best to make it reasonable to do so.

Conclusions

• Mental models make our lives and our work better.
• Arguments are a deep part of working with data.