How to Verify Your Assumptions At Small Sample Sizes

(This is a guest post by Luke Szyrmer, a Product Manager and Consultant, and author of the just released Launch Tomorrow, a book on the use of paid advertising to test, launch, and promote new products. You can find Luke on Twitter, LinkedIn or his website.)

Every block of stone has a statue inside it and it is the task of the [founder] to discover it. --Michelangelo

Your goal is to make money, not to publish peer-reviewed academic sociological research. While it's good to use the scientific method to test out a business idea, don't get lost in statistical minutiae. In particular, rigorous statistical analysis is a luxury that early stage founders can't afford. 99% certainty costs a lot of time and money. As a founder, focus on financial significance, not the rigorously high bar of statistical significance to a high level of certainty

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Guest Post: Pitching to Investors: A Lean Startup Perspective

(Andy Cars, founder of Seedcap and Lean Ventures, helps startups with strategy, business development, lean startup and fundraising, and large companies create and execute on their innovation strategies. If he’s not busy helping startups and enterprises, you can probably find him on Twitter and LinkedIn.)

Much has been written on how to pitch to investors. There's also plenty of resources available on Lean Startup. But how about pitching to investors from a Lean Startup perspective? Perhaps not so much.

Lean Startup is both a method and a mindset. If you are a Lean Startup practitioner it makes sense that your Lean Startup mindset should also to some extent carry over, or at least be noticeable, when you pitch to investors.

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Guest Post: Actioning Customer Discovery Interviews

(Nick Noreña, a Lean Startup Coach at TriKro, works with teams and organizations to help them implement Lean techniques in their daily business. An entrepreneur at heart, his favorite thing to do is work with early stage startups. If he’s not in his office in San Francisco, you can probably find him on a long bike ride, or on Twitter and LinkedIn.)

This is Part 2 of a two part blog post on effectively using the data you gather from customer discovery interviews. Check out Part 1 if you haven't already.

After a round of customer discovery interviews, I often find myself inundated with data. To be honest, it's a bit intimidating. I've tried lots of different approaches to organizing that data, from writing up long and verbose reports, to just giving the rest of my team the raw data and telling them to make sense of it (take a wild guess as to how well that worked). What I found was that while I had accrued lots of data I was always left begging for actionable insights.

So how do we actually interpret all of that data we worked so hard to collect?

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