Yes, that’s right. Those of us bred in academia, we are a bunch of pessimists. Or, at least industry people perceive us so, and you better get used to a new norm if not determined to go extinct. Get it? No? Ok, here’s an example. Your company just completed a clinical trial. Data shows that drug treatment groups seem to be slightly better off than a placebo arm, but not in a statistically significant way. Please describe the result to your Wall Street investors.
You are a smart PhD who ingrained scientific rigor into your DNA, so you play like this. “With p-value greater than 0.05, there are more than 1 in 20 chances that patient improvement in treatment arms are consequence of mere random drift, therefore ”. “You lost me”, your investment banker says, “So, is this a failure or success?” You poor graduate student-turned-self-hating-industry-pseudo-scientist responds “eh, sir, according to this statistical analysis”. “Failure or success!?”, goes the banker again. You sigh, and say “I suppose it is a failure because statistically those two groups are identical, blah blah blah…” Beep beep beep! FAILURE!!! You have just earned a big bold headline on newswires, had company stock tanking, mom and pop investors panicking, and guess what? You might no longer need to hate your new job. See, there is no “failure” in this world. There is a “possibility”. Capisce? You think it’s shady? Over-reaching? Hey, what do you know? You are just another academic egghead poking holes around. See, you lack the power of “positive” thinking. This trial will work next time. Or you will make it work. Now, it should’ve gone like this; “This is a smashing SUCCESS! There, you see some statistical anomaly, but it’s a SUCCESS anyway!!” Come loud cheers and confetti.

A Mean for an End

All right, I am exaggerating a bit. But not much. We shouldn’t miss fundamental differences on how you interpret your data, especially if that interpretation determines the fate of your enterprise. In academia, scientific precision trumps everything else. Years of gradate school teaches us to think critically when given an observation, and no matter how great your presentation looks, the merit of your work dictates the evaluation. You expect people will dissect your projects in and out, so much that people do not even need to use the term “devil’s advocate”. Plus, the last thing you want to achieve is to be praised for a false greatness. That is a losing proposition and you know that will eventually bite you back. Well, you cannot translate a rule of game from one league to another. Of course, no matter where you work, you always need to do quality science and be fair and square. But at the same time, there exist forces that polish the positive while downplaying the not-so-perfect in industry science. Public relations can triumph everything else.
Just or not, there are good reasons for such “cherry-picking” behavior. Science does not always produce a clear cut “success or failure.” When the stakeholders of your project demand the over-simplification, you have no other option but scratch their backs. When your data is in gray, you can afford to remain in gray in academia, but you are forced to pick up or down in industry. Then, why not choose one that will help you keep your job? If not given tomorrow, there is no next month, no next project, and no chance to prove yourself. In academia, scrapping a bad project only costs your time and efforts, and, yeah, some of other people’s money. In industry, stakes are much higher. The livelihood of the entire corporation and their members might be on the line. It is more the case for small biotech start-ups, where you have a limited portfolio, offering you no luxury of dropping a questionable initiative at your will. Also, people understand scientific ambiguity in academia, but the majority of audiences you deal with in industry lack the necessary patience. One minute into your discussion on chi-square tests, you will find your investment bankers with two hands on ears and humming lah lah lah. You don’t want to get bogged down in too much detail and bore them. You would rather give them a confidence, whatever that really means.

A Balancing Act

Let me be clear. By any means, I don’t imply that industry scientists routinely fabricate data or mislead other parties on their results. No quarter of science community would condone frauds. Differences in “tone” is, however, not unnoticeable, and one needs to be aware of the untold rules in order to make an effective transition from academia to industry. Cheerleading over sub-par quality science is not a way of doing business for us truth-loving academics, but could be a legitimate and effective survival tactic in industry. There is no one good answer on how to balance the power of honest, skeptical science and the American corporate culture of relentless optimism. Be assured, my fellows, that you are not alone in this quest for soul-searching and difficult compromises with reality.