C for Conclusion

From time to time, I give a lecture about most common mistakes in data visualization. In this lection, I say that not adding a graph’s conclusion as a title is an opportunity wasted

Screenshot. Slide deck. The slide says

In one of these lectures, a fresh university graduate commented that in her University, she was told to never write a conclusion in a graph. According to to the logic she was tought, a scientist is only supposed to show the information, and let his or her peer scientists draw the conclusions by themselves. This sounds like a valid demand except that it is, in my non-humble opinion, wrong. To understand why is that, let’s review the arguments in favor of spelling out the conclusions.

The cynical reason

We cannot “unlearn” how to read. If you show a piece of graphic for its aesthetic value, it is perfectly OK not to suggest any conclusions. However, most of the time, you will show a graph to persuade someone, to convince them that you did a good job, that your product is worth investing in, or that your opponent is ruining the world. You hope that your audience will reach the conclusion that you want them to reach, but you are not sure. Spelling out your conclusion ensures that the viewers use it as a starting point. In many cases, they will be too lazy to think of objections and will adopt your point of view. You don’t have to believe me on this one. The Nobel Prize winner Daniel Kahneman wrote a book about this phenomenon.

What if you want to hear genuine criticism? Use the same trick to ask for it. Write an open question instead of the conclusion to ensure everybody wakes up and start thinking critically.

The self-discipline reason

Some people are not comfortable with the cynical way I suggest to exploit the limitations of the human mind. Those people might be right. For them, I have another reason, self-discipline. Coming up with a short, concise and descriptive title requires effort. This effort slows you down and ensures that you start thinking critically and asking questions. “What does this graph really tells?” “Is this the best way to demonstrate this conclusion?” “Is this conclusion relevant to the topic of my talk, is it worth the time?”. These are very important questions that someone has to ask you. Sure, having a professional and devoted reviewer on your prep team is great but unless you are a Fortune-500 CEO, you are preparing your presentations by yourself.

The philosophical reason

You will notice that my two arguments sound like a hack. They do not talk about the “pure science attitude”, and seem to be detached from the theoretical picture of the idealized scientific process. That is why, when that student objected to my suggestion, I admitted defeat. Being a data scientist, I want to feel good about my scientific practice. It took me a while but at some point, I realized that writing a conclusion as the sole title of a graph or a slide is a good scientific practice and not a compromise.

According to the great philosopher Karl Popper, a mandatory characteristic of any scientific theory is that they make claims that future observations might show to be false. Popper claims that without taking a risk of being proved wrong,  a scientist misses the point  [ref]. And what is the best way to make a clear, risky statement, if not spelling it out as a clear, non-ambiguous title of your graph?

Don’t feel bad, your bases are covered

To sum up, whenever you create a graph or a slide, think hard about what conclusion you want your audience to make out of it. Use this conclusion as your title. This will help you check yourself, and will help your fellow scientists assess your theory. And if a purist professor says you shouldn’t write your conclusions, tell him or her that the great Karl Popper thought otherwise.


Is Data Science a Science?

Is Data Science a Science? I think that there is no data scientist who doesn’t ask his- or herself this question once in a while. I recalled this question today when I watched a fascinating lecture “Theory,  Prediction, Observation” made by Richard Feynman in 1964.  For those who don’t know, Richard Feynman was a physicist who won the Nobel Prize, and who is considered one of the greatest explainers. In that particular lecture, Prof. Feynman talked about science as a sequence of  Guess ⟶ Compute Consequences ⟶ Compare to Experiment

Richard Feynman in front of a blackboard that says: Guess ⟶ Compute Consequences ⟶ Compare to Experiment

This is exactly what we do when we build models: we first guess what the model should be, compute the consequences (i.e. fit the parameters). Finally, we evaluate our models against observations.

My favorite quote from that lecture is

… and therefore, experiment produces troubles, every once in a while …

I strongly recommend watching this lecture. It’s one hour long, so if you don’t have time, you may listen to it while commuting. Feynman is so clear, you can get most of the information by ear only.