Recently I have written a lot about our mind’s tendency toward causal thinking, and how this tendency can sometimes get our minds in trouble. We make associations and predictions based on limited information and we are often influenced by biases that we are not aware of. Sometimes, our brains need to shift out of our causal framework and think in a more statistical manner, but we rarely seem to do this well.
In Thinking Fast and Slow, Daniel Kahneman writes, “The associative machinery seeks causes. The difficulty we have with statistical regularities is that they call for a different approach. Instead of focusing on how the event at hand came to be, the statistical view relates it to what could have happened instead. Nothing in particular caused it to be what it is – chance selected it from among its alternatives.”
This is hard for us to accept. We want there to be a reason for why one candidate won a toss-up election and the other lost. We want there to be a reason for why the tornado hit one neighborhood, and not the adjacent neighborhood. Our mind wants to find patterns, it wants to create associations between events, people, places, and things. It isn’t happy when there is a large amount of data, unknown variables, and some degree of randomness that can influence exactly what we observe.
Statistics, however, isn’t concerned with our need for intelligible causal structures. Statistics is fine with a coin flip coming up heads 9 times in a row, and the 10th flip still having a 50-50 shot of being heads.
Our minds don’t have the ability to hold multiple competing narratives at one time. In national conversations, we seem to want to split things into 2 camps (maybe this is just an artifact of the United States having a winner take all political system) where we have to sides to an argument and two ways of thinking and viewing the world. I tend to think in triads, and my writing often reflects that with me presenting a series of three examples of a phenomenon. When we need to hold 7, 15, or 100 different potential outcomes in our mind, we are easily overwhelmed. Accepting strange combinations that don’t fit with a simple this-or-that causal structure is hard for our minds, and in many cases being so nuanced is not very rewarding. We can generalize and make substitutions in these complex settings and usually do just fine. We can trick our selves to believing that we think statistically, even if we are really only justifying the causal structures and hypotheses that we want to be true.
However, sometimes, as in some elections, in understanding cancer risk, and making cost benefit analyses of traffic accidents for freeway construction, thinking statistically is important. We have to understand that there is a range of outcomes, and only so many predictions we can make. We can develop aids to help us think through these statistical decisions, but we have to recognize that our brains will struggle. We can understand our causal tendencies and desires, and recognize the difficulties of accepting statistical information to help set up structures to enable us to make better decisions.
Novel Learning 