Many people, especially those handicapped with a graduate level education, like myself, think that data is only interesting and can only be acted upon if it is “statistically significant”. In the context of early stage Customer Development, I believe this is well-intentioned, but ultimately, misguided.
Evidence comes in a diversity of forms. It can be anecdotal, it can be in aggregate or it can be a trend line. If you take an open-minded approach to the types of evidence you’ll accept, and adjust for their biases/problems/problems accordingly, you’ll likely fare better in the chaos of startup-land than just simply jettisoning what you feel is low-quality data.
In my opinion, trying to make CustDev formulaic, well–that’s a road to perdition. Some of the techniques that may work now, might not work as well in two, five, ten years, so there is no point writing a hyper-specific rule book because no book can know exactly the context you are operating in and when you are operating there.
There are a lot of other good points made, generally pointing to the fact that not all business decisions can be reduced to a statistical A/B test, and how techniques and methods for customer development need to remain contextually sensitive to be useful. It's worth reading the full article.
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