The science of better data analysis: How to make better decisions with behavioral science

Overview

Exploratory data analysis (EDA) is a crucial step in any data-driven decision-making process. It involves analyzing and visualizing data to uncover patterns, relationships, and insights that can inform business decisions. However, EDA is not without its pitfalls. In this article, we will discuss some common pitfalls of exploratory data analysis and how behavioural science techniques can be applied to solve them.

Pitfall 1: Confirmation Bias

Confirmation bias occurs when analysts seek out and interpret information in a way that confirms their existing beliefs or hypotheses. This can lead to a narrow focus on certain aspects of the data while ignoring or downplaying others.

To overcome confirmation bias in EDA, analysts can use a technique called "devil's advocacy." This involves deliberately taking an opposing viewpoint and seeking out evidence that contradicts one's initial assumptions. This approach can help to reveal blind spots and prevent analysts from overlooking important patterns or relationships in the data.

Pitfall 2: Overfitting

Overfitting occurs when analysts create a model that is too complex for the data, resulting in a model that performs well on the training data but poorly on new data. This can happen when analysts try to fit too many variables into a model or when they use overly complex algorithms.

To avoid overfitting in EDA, analysts can use a technique called "regularization." Regularization involves adding a penalty term to the model to discourage overfitting. This penalty term can be adjusted to balance the tradeoff between model complexity and accuracy.

Pitfall 3: Correlation vs. Causation

Correlation and causation are often confused in EDA. Correlation refers to a relationship between two variables, while causation refers to a direct causal link between two variables. Just because two variables are correlated does not mean that one causes the other.

To avoid confusing correlation and causation in EDA, analysts can use a technique called "causal inference." Causal inference involves identifying and controlling for confounding variables that may be influencing the relationship between two variables. By doing so, analysts can determine whether a causal relationship exists between two variables.

Pitfall 4: Anchoring Bias

Anchoring bias occurs when analysts rely too heavily on the first piece of information they receive and do not adjust their analysis accordingly. This can lead to a narrow focus on certain aspects of the data while ignoring others.

To overcome anchoring bias in EDA, analysts can use a technique called "multi-perspective analysis." This involves approaching the data from multiple angles and considering different sources of information. By doing so, analysts can avoid being anchored to a single piece of information and can develop a more comprehensive understanding of the data.

Summary

In conclusion, exploratory data analysis is a critical step in any data-driven decision-making process. However, it is not without its pitfalls. By applying behavioural science techniques such as devil's advocacy, regularisation, causal inference, and multi-perspective analysis, analysts can overcome these pitfalls and develop a more comprehensive understanding of the data. Ultimately, this can lead to better-informed business decisions and improved outcomes.