A game of Battleship can teach us a lot about the value of failures in R&D.
Splash! And splash again! I’m engaged in a game of Battleship against my 7-year-old grandson. Competitiveness runs deep in our family and much to his amusement, he inflicts his first damage already at his second launch. I, on the other hand, need 26 tries to get my first hit. That’s a lot of splashes.
The loss is difficult to accept and therefore must lead to further reflection. How improbable is this? The math isn’t hard. On the 10 by 10 grid, 17 positions are reserved for the 5-ship fleet, the rest is water. In a simplified ‘urn model,’ the chance of having a starting sequence of 25 misses comes down to 0.5 percent. That’s a case of almost three sigma. Improbable, but apparently not impossible.
Or did I miss something? Or did I do something wrong? Should I adapt my tactics for getting my first hit, spreading my shots more evenly over the grid? Perhaps I should take a closer look at my simplified model: the targets aren’t scattered randomly across the board but have a finite size and the outer edges of the board pose additional limitations with respect to ship positioning. Or did my young opponent secretly change the rules of the game and did he fail to put his entire fleet on the board?
Nobody who hits a target at one of his first shots will stop to contemplate the luck involved. Only multiple failures produce a drive for deeper understanding. This mimics quite nicely the day-to-day reality of R&D projects.
Hence, splashes need to be treasured. Size doesn’t matter; faults are often hidden behind minor deviations in comparison to one’s expectations. So even small splashes are to be considered a valuable gift that shouldn’t go unnoticed, nor be ignored. The same goes for successes that come too easily.
Skill is needed to design tests that isolate and recognize small flags for what they really are: of significance or just a fluke? Proper education and hard-to-get experience, including hands-on practice from real-life cases, are much advised. Only then you may be sharp enough to find out that in your newly developed cryo-TEM, the slow image deterioration originates from a somewhat higher-than-expected thermal resistance of the sample holder at liquid nitrogen temperatures. Or, to your own astonishment, you finally start to understand why there’s a little bit of blue light coming out of an infrared diode laser. Or why, in some rare use cases, your instrumentation software malfunctions.
And, on a grander scale of things, it appears worth finding out why the classical black body radiation model deviates somewhat from experiments, Mercury’s position is slightly off in your predictions, the observable mass in your galaxy is different than expected from its rotation rate, and the CO2 concentration in the atmosphere is ever so slightly increasing year after year. The splash notion could even be extended to the analysis of management approaches and business failures.
So, after gallantly taking your loss, welcome splashes, and make sure to allow for time and effort to evaluate them properly, if needed post mortem. You’ll stand a better chance in your next replay.