Traditionally we say: If we find statistical significance, we’ve learned something, but if a comparison is not statistically significant, we can’t say much. (We can “reject” but not “accept” a hypothesis.)
But I’d like to flip it around and say: If we see something statistically significant (in a non-preregistered study), we can’t say much, because garden of forking paths. But if a comparison is not statistically significant, we’ve learned that the noise is too large to distinguish any signal, and that can be important.
So, to sum up, science is not about data; it’s not about the empirical content, about our vision of the world. It’s about overcoming our own ideas and continually going beyond common sense. Science is a continual challenging of common sense, and the core of science is not certainty, it’s continual uncertainty—I would even say, the joy of being aware that in everything we think, there are probably still an enormous amount of prejudices and mistakes, and trying to learn to look a little bit beyond, knowing that there’s always a larger point of view to be expected in the future.
We really have no idea what dolphins or octopi or crows could achieve if their brains were networked in the same way. Conversely, if human beings had remained largely autonomous individuals they would have remained rare hunter-gatherers at the mercy of their environments as the huge-brained Neanderthals indeed did right to the end. What transformed human intelligence was the connecting up of human brains into networks by the magic of division of labour, a feat first achieved on a small scale in Africa from around 300,000 years ago and then with gathering speed in the last few thousand years.
Take Salesforce for example. Right now it just presents data, and the human user has to draw her or his predictive insights in their heads. Yet most of us have been trained by Google, which uses information from millions of variables based on ours and others’ usage to tailor our user experience … why shouldn’t we expect the same here? Enterprise applications — in every use case imaginable — should and will become inherently more intelligent as the machine implicitly learns patterns in the data and derives insights. It will be like having an intelligent, experienced human assistant in everything we do.