Thinking Fast and Slow: Statistical Variability with Python and GPT4

Author(s): John Loewen, PhD

Originally published on Towards AI.

A Python and GPT-4 guided tour to decoding data variability
Dall-E image — impressionist painting of a dashboard with maps and charts

We tend to simplify things quickly, but we can also think slowly and deal with complexity (when we want to).

In his book “Thinking, Fast and Slow,” Daniel Kahneman explains our struggle with understanding data variability.

What does this actually mean? Humans have a tendency to oversimplify complex data, often missing its inherent variability.

System 1 thinking leads us to quick, intuitive conclusions, while System 2 thinking requires slow, deliberate analysis to grasp the complexities and variations in data.

Essentially, we struggle to balance our instinct for simplicity with the need for deeper, more accurate understanding of data variability.

We can use GPT-4 with Python and CO2 emissions data to demonstrate how we can start with System 1 and move to System 2, allowing us to get to grips with this complexity (without getting frustrated).

Let’s work through four practical coding examples to get a clearer view of how to approach data variability, improving your ability to analyze, and make sense, of the details.

We will start by generating a simple visualization to give an intuitive, immediate understanding of our data.

A common method here is with a simple line chart.

We can show an overall trend for… Read the full blog for free on Medium.

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Published via Towards AI