Data is key to decision making. Yet we are rarely faced with a situation where things can be put in to such a clear logical form that we have no choice but to accept the force of evidence before us. In practice, we should always be weighing alternatives, looking for missed possibilities, and considering what else we need to figure out before we can proceed.
Arguments are the glue that connects data to decisions. And if we want good decisions to prevail, both as decision makers and as data scientists, we need to better understand how arguments function. We need to understand the best ways that arguments and data interact. The statistical tools we learn in classrooms are not sufficient alone to deal with the messiness of practical decision-making.
Examples of this fill the headlines. You can see evidence of rigid decision-making in how the American medical establishment decides what constitutes a valid study result. By custom and regulation, there is an official statistical breaking point for all studies. Below this point, a result will be acted upon. Above, it won’t be. Cut and dry, but dangerously brittle.
The results can be deadly. Between 1999 and 2004, an estimated 60,000 people died from taking Vioxx, a pain-killer marketed for arthritis. Evidence came to light early on that the drug increased the risk of heart attack. But because official decision-making was based on a break point and not nuanced argument, the drug stayed on the market for years. Nuanced reasoning can save lives.
If this kind of procedure sounds familiar, it’s probably because it’s the dominant way that people use data across business, government and academia. The numbers are up? The graph trends down? The slope is “significant”? Congratulations, according to the absurdly low standards that prevail in most places, you’re bolding using data! Here is a gold star.
Thinking explicitly about arguing has traditionally been a skill of humanities professors, lawyers, and the occasional elder scientist. If data is going to be our new guiding light, then as data scientists, mangers of data scientists, or people who want to better use data in pursuit of excellence in any field, we need to get more comfortable with the tools of arguments.
It’s become common knowledge across business, the non-profit sector, and academia that we are “swimming” in data, yet constantly “falling behind” on making good use of it. Depending on who you ask, the latest tools or newest techniques are the cure-all that we need to turn these raw facts into insights.
What’s missing from all of these discussions is a hard look at how people actually move from data to decision. How does data compel someone to change their mind? Even more importantly, how does data compel someone to act differently?
This is an old question, and it has an old answer. The answer is rhetoric, though perhaps not the way that you may think of the word. The ancient Greeks understood that studying how and why people came to be convinced of things was a worthwhile field in and of itself. Rhetoric is the study of arguments presented by one person to another. It has seen a resurgance in the last fifty years, after a quiet period stretching from the 17th century onward. Dialectic, its sibling, is the study of how arguments are conducted between two people holding different viewpoints.
Historically, “rhetoric” didn’t have the connotation of flashy presentation (which is how the word is often used today). Instead, traditionally rhetoric has been the study of all aspects of argumentation: inventing arguments, arranging arguments, understanding the goals of an argument, and, ultimately, making an intelligent presentation.
Understanding arguments helps us think up new ideas, helps us weigh possibilities against each other, and helps us think critically about what people are trying to convince us to say and do. Arguments are everywhere. Every time you play around with a spreadsheet, or make an exploratory graph, or do some quick tabulations, there is an argument, or a fragment of an argument, at play. All arguments have structure. Understanding that structure is powerful.