> For the complete documentation index, see [llms.txt](https://mada.gitbook.io/experimentation-field-guide/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://mada.gitbook.io/experimentation-field-guide/why-experimentation.md).

# Why Experimentation?

## TLDR;

* Plans don’t guarantee outcomes. Experiments can increase the likelihood of success.
* Experimentation helps the affirmative practice of design call out assumptions
* Documented experiments research insights easier to share.
* Experiments can act as beacons and activators of collaborative work.

## Background

In an increasingly complex world, we need new forms of practice which enable us to cope with and thrive in the greater interconnection and less clear causality. 

In response to calls for greater evidence of impact, Research Labs need tactics and strategies to work with and in complexity. Experiments offer a way to do this that aligns with design’s predisposition to take action and be future oriented. 

![](https://3314128292-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-Ljru8tSYycVPAJyc14_%2F-LjtGGHAsG1XT5Kbo7ve%2F-LjtGIVaEevlAqZpONmj%2FComplexity.jpg?alt=media\&token=b1c9609a-c0c0-47e3-b6a7-953ce403d574)

Planning-centric approaches (characterised by analyse-to-predict), become less effective as the complexity increases, variables multiply and causality becomes less predictable. 

In contrast, rigorous experimentation approaches (characterised by prototype-to-learn) becomes more useful as complexity increases. Each sacrificial prototype increases the likelihood of finding a good outcome. To be clear, here we’re talking about a form of *Design Experimentation*, not Scientific Experimentation.