Exploratory Data Analysis and Critiques of Significance Testing

This course will introduce students to the approach called "Exploratory Data Analysis" (EDA) where the aim is to extract useful information from data, with an enquiring, open and sceptical mind. It is, in many ways, an antidote to many advanced modelling approaches, where researchers lose touch with the richness of their data. Seeing interesting patterns in the data is the goal of EDA, rather than testing for statistical significance. The course will also consider the recent critiques of conventional "significance testing" approaches that have led some journals to ban significance tests.

Students who take this course will hopefully get more out of their data, achieve a more balanced overview of data analysis in the social sciences.

• To understand that the emphasis on statistical significance testing has obscured the goals of analysing data for many social scientists.
• To discuss other ways in which the significance testing paradigm has perverted scientific research, such as through the replication crisis and fraud.
• To understand the role of graphics in EDA

• University Students from Tier 1 Departments
• Further details regarding eligibility criteria are available here

It will be useful for students to have completed the introductory statistics course (FiAS), but this module will have little technical content.

• Marsh, C. & Elliott, J. Exploring Data: An introduction to Data Analysis for Social Scientists, Polity Press, 2008.
• https://en.wikipedia.org/wiki/Exploratory_data_analysis

This module is not assessed.

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