One of the most important skills for better critical thinking is learning to resist binary dichotomies on important, complex topics. We’ve categorized the most common types of binary thinking and paired them with forms of nuanced thinking that can be used as an antidote to these rationality traps.

Our brains, too often, are dichotomizing machines. We tend to simplify the world into true or false statements, good or bad categories, and is or is not assertions.
This dichotomizing tendency works well when it comes to relatively simple topics like:

  • 1+1=2 (true) vs. dolphins control our planet (false)
  • viruses (bad) vs. puppies (good)
  • a fedora is a hat; a fedora is not a bat

But when it comes to important, complex topics (and especially ones that are political, emotional, or related to our identity), thinking in binary often impairs our ability to figure out what’s what. It’s hard to be accurate when you’re thinking with just one bit of information. This is where nuanced thinking comes in; resisting the urge to divide matters one way or another can result in a much more realistic picture of the world. 
Here are three types of binary thinking, followed by three kinds of nuanced thinking that can be used to combat these tempting binary dichotomies. 

Dichotomy 1: The Truth Binary

The Truth Binary is when we view a statement as simply true or false, correct or incorrect. But on complex topics, simple viewpoints are often partially true and partially false, or true some percent of the time and false the rest of the time. Furthermore, our knowledge of truth is limited, so we should have degrees of confidence rather than certainty. If we want to be right more often, we should have thoughts like “I’m 90% confident that…” or “I’m 60% confident that” rather than “I believe that…”
Examples of complex topics where people often fall into the Truth Binary:

  • I believe / don’t believe that the government is incompetent
  • I believe / don’t believe that harsh prison sentences for violent crime make society safer
  • I believe / don’t believe that the people of the UK benefit by leaving the EU
  • I believe / don’t believe that we should end the COVID-19 lockdowns as soon as possible
  • I believe / don’t believe in the effectiveness of western medicine
  • I believe / don’t believe that Trump will win the next election
  • I believe / don’t believe that the power of the United States will decline

Solution 1: Probabilistic Thinking

The solution to the Truth Binary is Probabilistic Thinking, where we consider our level of confidence in our beliefs, avoid having 100% confidence in anything, and consider in what situations a view could be true vs. in what situations it could be false. This kind of thinking is more likely to get us closer to the truth than thinking in a truth binary.

Probabilistic Thinking involves asking ourselves questions like:

  • What do I think the chance is that this viewpoint is correct?
  • How often do I expect that this viewpoint is correct, and how often would I expect it to be wrong?
  • How surprised would I be if it turned out I was mistaken on this issue?
  • Would I bet a meaningful amount of money, only a little, or none at all that this view is correct?

You can use our free tool on “the question of evidence” if you’d like to become a better probabilistic thinker.

Dichotomy 2: The Goodness Binary

The Goodness Binary is when we view things as either good or bad, positive or negative, or moral or immoral, when, in fact, there is most often a mix of “good” and “bad” features when we consider complex, hotly-debated topics (even if, all things considered, one side really is better).

Examples of complex topics where people often fall into the Goodness Binary:

  • Anyone who voted for X is fundamentally good / bad
  • Nuclear power is good / bad
  • Capitalism is good / bad
  • Socialism is good / bad
  • China is good / bad
  • Technological progress is good / bad
  • Antidepressants are good / bad to take if you’re depressed
  • Religion is good / bad
  • That public figure I love / hate is good / bad
  • That book is good / bad

Solution 2: Grey Thinking 

The solution to the Goodness Binary is Grey Thinking, where we accept that good things usually have some bad elements, that bad things usually have some good elements, and that many things lie somewhere in the middle. Grey Thinking makes us more effective at identifying solutions (because it allows us to better consider necessary tradeoffs) and helps us avoid accidentally harming the world through misguided good intentions.

Grey Thinking involves asking ourselves questions like:

  • What are the pros and cons of this?
  • Who benefits from this, and who is harmed?
  • What value does this thing I dislike create, even if this sort of value is not the kind of value I most care about?

Dichotomy 3: The Identification Binary

The Identification Binary is when we view things as either a member of a class or not a member of that class, when in fact, almost every categorization admits edge cases that lie between categories, or fails to categorize some cases.

Examples of complex topics where people often fall into the Identification Binary:

  • You’re on our side, or you’re against our side
  • You’re male, or you’re female
  • That’s a cult, or it’s not
  • She’s right-wing, or she’s not
  • He’s a criminal, or he’s not
  • You’re gay, or you’re straight
  • He’s a terrorist, or he’s not
  • She’s racist, or she’s not
  • They’re an American, or they’re not 

Solution 3: Multi-factor Thinking

The antidote to the Identification Binary is Multi-factor Thinking, where we consider the degree to which something has different factors. Multi-factor Thinking helps us see people and things as they really are, rather than oversimplifying them or misjudging their characteristics.

Multi-factor Thinking involves asking ourselves questions like:

  • In what ways is this case similar or different from these categories?
  • Is that example better thought of as lying between two (or three) categories, rather than as being right in the middle of one category?
  • If I ignore labels for a second, what traits does this case have?

With all this in mind, hopefully, you can see how nuanced thinking is much more likely to give us an accurate picture of the world than using binary dichotomies will.

In summary:

  • If you want to figure out what’s true in the world, avoid the Truth Binary on important, complex issues, and use Probabilistic Thinking instead. Ask yourself how sure you are and how often this thing is true and avoid 100% certainty.
  • If you want to improve the world (and not accidentally cause harm), avoid the Goodness Binary, and use Grey Thinking instead. Ask yourself what the pros and cons are and consider what’s good and bad about each thing.
  • If you want to see people and things as they really are, rather than oversimplifying or misjudging, avoid the Identification Binary, and use Multi-factor Thinking instead. Ask what ways this thing is similar or different to a category, how it might blend multiple categories, and what traits it has irrespective of categories.

Of course, these three methods of nuanced thinking shouldn’t be used all the time. For simple scenarios and observations, binaries can be good enough and can save you effort. Some things aren’t important enough to spend the time it takes to get a nuanced picture of things. There are also times when you just need to get along with your group, rather than trying to see probabilities, shades of grey, and multiple factors in everything. But when a topic is important and complex, and you care about having accurate beliefs, using these three nuanced thinking techniques will help greatly.

Spencer Greenberg is a polymath, entrepreneur, and mathematician, with a passion for improving human decision-making. He has a Ph.D. in applied math from NYU, with a speciality in machine learning.

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  • Greenberg is a polymath, entrepreneur, and mathematician, with a passion for improving human decision-making. He founded Spark Wave, a startup foundry which creates novel software products designed to solve problems in the world. He also founded which offers free tools and training programs designed to help improve decision-making, increase positive behaviors, and reduce cognitive biases. Spencer has a Ph.D. in applied math from NYU, with a speciality in machine learning. Previously, he co-founded a quantitative investment firm, where he designed algorithms to make daily predictions about thousands of stocks. Spencer's work has been featured by numerous major media outlets including the Wall Street Journal, the IndependentFast Company, and the Financial Times.