“But no one, no matter how intelligent, could make good inferences from bad data.”

“We’re inferring from an absence of data,” Jacque said. “That’s lousy science.”

“Intelligence is not to make no mistakes, but quickly to see how to make them good.”

“It is greed that makes us human as it could be for good or bad.”

“Traditional statistics is strong in devising ways of describing data and inferring distributional parameters from sample. Causal inference requires two additional ingredients: a science-friendly language for articulating causal knowledge, and a mathematical machinery for processing that knowledge, combining it with data and drawing new causal conclusions about a phenomenon.”

“It may sound trite, but using the weapons of the enemy, no matter how good one's intentions, makes one the enemy.”

“The conjuror or con man is a very good provider of information. He supplies lots of data, by inference or direct statement, but it's false data. Scientists aren't used to that scenario. An electron or a galaxy is not capricious, nor deceptive; but a human can be either or both.”

“The Good, the Bad and the Ugly: data brokers are like the Wild West -- untamed and without governance.”

“Human mind is capable of making up excuses to justify actions no matter how bad they were”

“…git actually has a simple design, with stable and reasonably well-documented data structures. In fact, I'm a huge proponent of designing your code around the data, rather than the other way around, and I think it's one of the reasons git has been fairly successful […] I will, in fact, claim that the difference between a bad programmer and a good one is whether he considers his code or his data structures more important. Bad programmers worry about the code. Good programmers worry about data structures and their relationships.”