How data analysts experience cognitive biases and how to recognise them: Part 6

26 June 2018

Cognitive biases: We all have them and we regularly encounter them in everyday life. But how do data analysts actually experience these biases? And how can you recognise them? In this series we will discuss cognitive biases in the context of analysts. Each blog picks out 3 biases from 20 categories.

In this blog, we have three biases from the “Need to Act Fast” Spectrum: the Ambiguity Bias, Appeal to Novelty and the False-Consensus Effect.


Ambiguity Bias

People don’t like unpredictability – and therefore people often confuse unpredictability (not having information) with negativity (having negative information). For example: you are asked to make a real-time dashboard out of a static sales report in Excel. You’ve never done it before but you know a colleague of yours has done something similar in Tableau and it took him 5 days. You can also do it in PowerBI, but you have no idea how long that would take. There’s a strong chance you’ll choose to make the dashboard in Tableau because there is less ambiguity in that option.


Appeal to Novelty

Put simply, this bias says that we feel attracted to new things and therefore we perceive things that are new or modern as being better than old things or the status quo. It is based on this logic: we have been using churn model X ever since 2015. We have made a new one, churn model Y. So, churn model Y is better. As a result of the Appeal to Novelty, new things are often implemented without checking whether they are actually any better. So you should investigate for yourself whether you want to learn Python 3 because it is genuinely better or because it is a newer version than Python 2. And when a colleague of yours is desperate to install the new version of Windows – you can ask him critically whether it really is a better version :-).


False-Consensus Effect

This is a bias you find everywhere: People overestimate the likelihood that their own opinions, preferences and values are the norm and that other people share them too. In other words: everybody does and thinks what I do and think. This bias especially causes frustration (for me anyway) when reading someone else’s programming script. Because surely everybody uses tabs and comments to keep their script easy to read. Fortunately, this bias doesn’t only cause frustration, it also causes amused bewilderment. If at lunch your colleague says she thinks it’s completely normal to have a cheese and chocolate spread sandwich (and can’t understand that everybody doesn’t eat that), that is also a false consensus. You can spot this bias in others when they say things like: “But surely everyone… [enter opinion-preference-value here]. I can’t understand that other people don’t too.”


Cartoon of biases series 3-panel 1: churn analysis

Cartoon of biases series 3-panel 2: marketing - customers


Also read the other parts:

Part 1: Self-relevance effect, Confirmation bias and the Bias blind spot.

Part 2: Availability heuristic, Contrast effect and Clustering illusion.

Part 3: Essentialism, the Positivity Effect and the Appeal to Probability Fallacy.

Part 4: Curse of Knowledge, the Hindsight Bias and the Hard-Easy Effect.

Part 5: Sunk Cost Effect, the IKEA effect and the Google Effect.



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