Choosing the right experiment

Work

One of the things I spend a significant amount of time on is devising, designing and running experiments on various different ideas for new concepts. It is both fun and challenging.

The challenging part is mostly about not reverting to the same 2-3 types of experiments and use them again and again. But because it is wrong to do so, and you might develop bias. But also because there are actually a lot of different ways, you can design and run experiments based on what kind of hypothesis, you’re trying to (dis)prove.

For that reason I have built yet another Excel-model; a simple database of all the different experiments, we know and can run with titles, applicable stages, ‘how to’-recipies and our know-how and experience in running them with valid results. Using the filter option on that one quickly allows me to narrow down the list of useful experiment-types for any given idea, broaden our horizon – and generate better results.

It is really that straightforward.

(Photo: Pixabay.com)

Excel’ing in assumptions

Modelling

What do you do, when you are a big fan of Assumptions Mapping as brought forward to David J Bland of Precoil, but you are not into doing a lot of Post It’s on a wall? You of course build an Excel model for it.

I have been using Assumptions Mapping for a couple of years now, but I have always struggled to use it in fx a workshop setting, because the concept with the quadrant, identifying knowledge gaps etc is foreign to many people. My experience is that it often goes much better if you just have a conversation, ask questions and plot down the answers.

So, I build a model in Excel that does exactly that. It lets you ask all the questions, make notes and score each answer based on the degree you have hard data on it and its criticality to the overall project. Once scores, the model will build a scatter chart with the correct labels, and in an instant you will have a visualization, you can work from. Cool, huh?

(Illustration: Visualisering fra modellen)