Reflect on Experiments
The vocabulary of experimentation plays an important role in setting expectations for innovation: it’s imperfect, it’s fraught with necessary missteps, it’s largely trial-and-error, it’s a high-volume and low-probability endeavor.
One important note is, experimentation should not be only forward-facing. A deliberate practice of reflection harvests the insights we glean from experiments, and amplifies the impact that experimentation has on our exploratory trajectory.
Steve Martin gives a master-class in reflection in his memoir, Born Standing Up: “Following the advice in Showmanship for Magicians, I kept scrupulous records of how each gag played after my local shows for the Cub Scouts or Kiwanis Club. ‘Excellent!’ or ‘Big laugh!’ or ‘Quiet,’ I would write in the margins of my Big Indian tablet; then I would summarize how I could make the show better next time...”
The great Steve Martin kept notes of how gigs for local Cub Scout Troops went! He found ways to improve his bits regardless of the apparent prestige of the venue. Interestingly, it is this very practice that planted the seed for what would become the bedrock of his unique appeal. He continues, “...I was still motivated to do a magic show with standard patter, but the nice response to a few gags had planted a nagging thought that contradicted my magic goal: They love it when the tricks don't work...”
That little light bulb, which sent his career in an entirely new direction, went off specifically because Martin maintained a discipline of reflection on his prolific experimentation schedule.
To extract the most value from any experiment, or from practice in general, we must be deliberate about seeking to understand the “why” behind the “what” from the data that an experiment creates.
When blocking time to craft experiments, make sure to block time to gather and evaluate the data as well.
Related: Close the Loop on Experiments
Related: Get Scientific
Related: Consider the Odds
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The fine folks over at Section recently interviewed me about some common mistakes I’m seeing folks make when they use AI. One of the biggest ones? The tendency to expect too little, and get just that.