Mean Time Between Hypothesis and Insight

I found this quote amongst growth related literature and loved it.

anyone in the startup community will tell you wonderful quotes about agile methodology and scrum and ‘move fast and break things’

But moving fast isn’t just a mindset, tools are only as effective as the people operating it, and only as useful as the processes they automate.

So i really like this concept of trying to focus on shortening the Mean time between hypothesis and insight since that accurately reflects what we’re attempting to do.

We want to have a clear and concise hypothesis that is align with impacting part of the business to drive result. we want it to be significant enough to merit our time, clear enough that parties are aligned, scientific and evidence based enough that we can measure, and concise enough that we can conduced specific experiments and initiatives quickly. There’s no point moving fast on guesses and subjective theories that are complex and hard to measure, you’ll end up bottlenecked into long experimentation cycles.

Insight comes from clarity of the question and accurate data collection, so a good hypothesis and a strong experiment and data capture are key. we need to have some idea what answer we are looking for.

as to shortening the mean-time- this relates to our experiments, the need to be concise, clear, collect sufficiently meaningful data and be quick enough to allow us some insight within days. we want to be able to only collect just enough data to give is signal, before adjusting to a new hypothesis and a new experiment based on the insight.

Its the confluence of strong hypothesis, and efficient experiments with short timeframes that results in meaningful insights.

imagine being able to rollout and collect insights on a hypothesis each week, how much improvement could be gained?

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