Data Science by Zachariah Voulgaris

Author:Zachariah Voulgaris [Voulgaris, Zachariah]
Language: eng
Format: epub
Publisher: Technics Publications
Published: 0101-01-01T00:00:00+00:00

The Importance of Sensitivity Analysis

No matter how well we design and conduct our experiments, there is always some bias there, be it in the data we use, the implicit assumptions, or in how we analyze the results and draw the conclusions of these processes. Unfortunately, this bias is a kind of mistake that often goes by unnoticed, and unless we are aware of this issue and take action, our conclusions may not hold true in the future.

Imagine that you created a model based on the answers of certain questions about the data at hand, only to find out that these answers were not as reliable as they seemed. You probably would not want to put such a model into production!

These issues and uncertainties that may threaten the trustworthiness of your work are bound to disappear with just a bit of extra validation work in the form of sensitivity analysis. As a bonus, such a process is likely going to provide you with additional understanding of the experiments analyzed and help you gain deeper insight into the dynamics of the data you have. The fact that many people don’t use sensitivity analysis techniques because they don’t know much about it should not be a reason for you to avoid it too.

There are two broad categories of techniques you can use for sensitivity analysis depending on the scope of the analysis: global and local. We will look into each one of these later on in this chapter. Before we do so, let’s take a look at a very interesting phenomenon called the butterfly effect, which captures the essence of the dynamics of such systems that need sensitivity analysis in the first place.

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