“Zeroes and ones will take us there!”
– Jesus Jones (1993)

A fun side story,
One of my favorite parts of strategy is gathering data.
It’s an underappreciated facet of being a good strategist, 
But it is not without its flaws. 

In clinical research, we use data to understand how and where patients have been recruited 
And use that data to predict what might happen in future studies.
Depending on who you talk to, some will swear by the mean, while others the median.
[And yes, there’s still the mode—but I only accept Depeche.]

The truth?  There is no “right way”
Because every scenario is unique.
Strategy isn’t about finding one right number;
it’s about viewing the data through the best lens for the job.

For example I was developing a proposal built on a handful of similar oncology trials.*
One dataset showed an unusually high mean (or average) enrollment rate—
which, on paper, meant shorter timelines and lower costs.
But a few other trials had much longer timelines, and much lower rates.
Something felt… amiss.
[And when something feels amiss, you know it.]

Sure enough, the median enrollment rate of that study was far lower—
A difference between one patient per month and one patient every four.
The culprit? An outlier investigator enrolling seven times more patients than the rest.
A great outlier, but an outlier nonetheless.
That single site inflated the average,
And risked me over-promising on future performance.

In the end, after discussing risk tolerance with the study teams,
I used a rate closer to the median.
It did require a longer timeline, but one with a higher potential accuracy.

My takeaway?
Both rates were valuable.
The average highlighted where high-performers could accelerate enrollment.
The median provided more realistic expectations across the full network of potential sites.
Together, they made for a more well-informed, data-driven decision,
At least through the World in My Eyes. 

“Similar”
always deserves an asterisk—study design, drug class, competition, investigator
excitement, and a dozen other factors all shape the outcome. But that’s a post
for another day.