previous summer, he hadn’t been allowed near one, so his anticipation
was high when a Chinese scientist named Ran Hu showed him one of
the machines with its black-and-white case removed. Standing next to
Tyler was Aruna Ayer, his supervisor. Aruna was just as curious as he
was: in her previous role as head of the protein engineering group, she
had never seen an Edison either. As Ran did a quick demonstration,
Tyler and Aruna weren’t sure what to think. The device seemed to
consist of nothing more than a pipette fastened to a robotic arm that
moved back and forth on a gantry. Both had envisioned some sort of
sophisticated microfluidic system. But this seemed like something a
middle-schooler could build in his garage.
Trying to keep an open mind, Aruna asked, “Ran, do you think this
is cool?”
In a tone that implied she did not, Ran replied, “I’ll let you decide
for yourself.”
When its case was back on, the Edison did sport a touchscreen
software interface, but even that was a letdown. You had to pound on
the screen’s icons to get it to work. Tyler and some other members of
the group joked that Steve Jobs would have rolled over in his grave if
he had seen one of them. Tyler felt a wave of disappointment wash
over him but beat it back by telling himself that the 4S, the next-
generation device he had heard was in the works, was probably much
more intricate.
Soon, there were other things that began to trouble Tyler. One type
of experiment he and Erika were tasked with doing involved retesting
blood samples on the Edisons over and over to measure how much
their results varied. The data collected were used to calculate each
Edison blood test’s coefficient of variation, or CV. A test is generally
considered precise if its CV is less than 10 percent. To Tyler’s dismay,
data runs that didn’t achieve low enough CVs were simply discarded
and the experiments repeated until the desired number was reached.
It was as if you flipped a coin enough times to get ten heads in a row
and then declared that the coin always returned heads. Even within
the “good” data runs, Tyler and Erika noticed that some values were
deemed outliers and deleted. When Erika asked the group’s more