Me: I think the big bites can burn your mouth more than small bites, because there are more molecules–those little balls bouncing around–that can bump into your mouth and warm it up than there would be in a smaller bite. At least, that’s my guess.
My young daughter: That’s your hippopotamus?
Me: Yes. That’s my hippopotamus.
In an earlier post, I wrote that more established teams (I called them lean grownups) should borrow the hypothesis validation thinking found in Lean Startup. I left off with the question: where should lean grownup teams find hypotheses to test, beyond the value and growth hypotheses that they may have for the most part already validated? In this post, the answer: hunt for hypotheses in your assumptions.
Every bit of work you do rests on assumptions, and every assumption carries some risk that it is false. Certainly, you can’t experiment on every assumption. (Let’s see, I have to go to the store to get some eggs, I assume gravity is still working so I don’t just fly off the planet, but I should run an experiment to be sure.) However, it is feasible to test at least the riskiest assumptions.
Two ways you could assess the risk of an assumption:
- Use the traditional formula: likelihood times impact. For example, gravity failing to work would be enormously impactful, but the likelihood is zero, so the risk is low. On the other hand, the likelihood that the store’s ATM is broken (you don’t have cash and they don’t take cards) is fairly high, as you’ve noticed it frequently is out of order. The impact would be a fair amount of extra time, as you’d have to turn around and go the the next closest store in the complete opposite direction. Multiplied, these suggest a riskier assumption than the one about gravity.
- A simpler way to do it is to ask yourself, “What’s the worse that could happen if my assumption was wrong?” If you don’t mind that scenario coming true, you might choose to chance it. Otherwise, find some way to test it.
Once you have found a risky assumption that you want to test, make a hypothesis and write it down. How will you test it? What will you do? What do you predict will happen as a result of what you do? How will you measure the accuracy of your prediction? Is the story the cheapest and fastest way to run the test responsibly? And of course: does the prediction come true? (Why write it down? Writing things down helps you see the error in your thinking. Also, in groups, it decreases the chance of thinking you are talking about the same thing when in fact you are not.)
This may seem like overkill, but if we don’t get into the habit of explicitly stating these hypotheses and testing them, don’t we risk building something unnecessary? Or building the wrong thing? And if we do define these hypotheses clearly, don’t we increase our chances of finding cheaper ways of assessing them? At the very least, might we not progress through our work a little more systematically?
To sum up: look through your assumptions, identify the riskiest ones, and then write down the hypotheses hidden in those assumptions.
Next post: tricks for finding assumptions you might be missing.