Thinker’s Game: importance of critical thinking and how to develop critical thinking skills

It is hard to overstate the importance of critical thinking in today’s knowledge economy. In this post, you will learn about critical thinking skills, how to develop them, and resources to help you further.

Imagine yourself taking the blame for assessing that Iraq possessed WMDs. Various committees found that managers “did not encourage analysts to challenge their assumptions, fully consider alternative arguments” and that they were focused on the end results and not on the process [Moore: Critical thinking and intelligent analysis]. Thankfully most of us are not in a position to provide recommendations that affect national security, but if they did, would you change your thinking process?

Importance of critical thinking

On his way to work, a villager met a wolf. Hungry and desperate, the wolf pleaded to the villager to give it some work. Although the villager initially resisted, due the wolf’s insistence, the villager relented and took the wolf home. He asked the wolf to guard his shack and his belongings. After sometime, just when the villager welcomed a child in his family, he started trusting the wolf.

One day, the villager and his wife had to go out but they couldn’t take the child with them. They decided to ask the wolf to look after the baby. The wolf, very excited and happy about the trust that family showed, agreed.

A few hours later, when the villager came back, he was aghast by the scene. He saw the wolf panting with blood on its mouth. Without even thinking for a minute, he took his axe and killed the wolf.

As he rushed inside, he saw that his child was sleeping sound in the crib, but next to it was a venomous snake left in pieces. Needless to say that he was filled with grief and sorrow and with only one thought: “What if had I delayed my judgment for a few minutes?”

Now, the question is: have you been guilty of jumping to a conclusion too quickly? Your colleague brings you an idea, and you take your axe out and boom: list 100 reasons why it can’t be done. You do not delay your judgment. You do not let the ideas form fully. You do the intellectually lazy thing and say “interesting” and walk out. Saying “interesting” is exactly like a kid’s response to doing something mischievous: “I don’t know who did it”. We take the easy route and instead of challenging our intellectual capacity we answer with intellectual dishonesty. [That response is not unlike when people tell me how much they love running or working out: I say “interesting” and run as far away as possible.]

Thinking about the problem is the first step in any sound analysis. Excellent critical thinking involves limiting self and other biases, evaluating many alternatives, and presenting multiple solutions. Analysis is hardly a one-shot process: take data, select favorite technique, and apply it using favorite software. Good analysts will first ask “why?” rather than asking “how?”, and after seeing the results, will ask “so what?” rather than saying “interesting.” The ultimate objective of analysis is to iterate continuously and not be satisfied with the present. Rather than asking “which is the best technique to solve my problem,” we must focus on maximizing information to time ratio.

Programming, specialized software or technique are irrelevant once the right question for the analysis is formed. Good analysts possess excellent critical thinking skills, which they use to remove the insignificant (noise) details from the given information to focus on significant (signal) details and come up with multiple possible solutions. They don’t stop there, however. While thinking of those possible solutions, they also think how to come up with an optimal solution. [Moore: Critical thinking and intelligent analysis]. In short, they focus not only on the end results, but also on the process.

Stages of Analysis

In their book Introduction to Strategy, Liedtka et. al say: “in any analysis, it is important to be prepared to articulate your major conclusions and provide evidence and analysis in support of them. You should be prepared to not only present your own analysis and conclusions, but also respectively challenge and extend the viewpoints of others … strategic analysis requires the honing of your skill in logic and argumentation.”

Similar to the analytics maturity model for an organization, I propose a model, given in the table below, for maturity of analysis. At the bottom of this model, we have day-to-day trivial decision making, where given a problem, we come up with a solution rather quickly. An example where this type analysis would be sufficient for most people: where to stop to buy a cup of coffee on the way to work? [I say most, because I’ve gotten into trouble with my wife for taking too long to decide which coffee shop to go to.]

Most of us in the knowledge industry spend a lot of time conducting analysis from the second stage. This stage consists of better planning as to how to answer the given question, generating multiple questions, collecting enough information, and producing a result. An example where this type of analysis would be required: which car to buy?

The analysis stage is advanced when the analyst starts challenging her own findings. This is where the good analysts start differentiating themselves from others. In his book Good to Great, Jim Collins described good leaders as the ones who seek self-actualization which is a process of understanding your limits and planning for your growth. It is very difficult to criticize your own work if you do not seek self-actualization.

The last stage of analysis is where the analyst generates multiple and competitive hypotheses, broadens the thinking to include other possible solutions, and iterates the process. While discussing critical thinking in his book Critical Thinking and Intelligence Analysis, David T. Moore says that a thinker “reflects on the quality of the reasoning process simultaneously while reasoning to a conclusion.” It is not enough to generate a quality recommendation, critical thinking demands questioning the thinking process itself. Analysis at this stage will provide key recommendations and generate questions that have not been asked yet.

Table: Stages of analysis.
improve your critical thinking. stages of thinking

How to improve your analysis

Hadley Wickham, a professor of statistics at Rice University and a chief scientist for RStudio, believes that critical thinking can be taught. In one of his courses, he grades students on three components: curiosity, skepticism and organization.
In his own words:

these reflect what I believe to be the three key attributes of a statistician: they should be curious about data and able to creatively apply old tools in new ways; they should be sceptical about their findings …; and they should be able to present their findings in an organised manner that guides the audience from raw data to results.

As the first step in improving your analysis, assess your analysis using the grading rubric given in Hadley’s paper: “Using visualisation to teaching data analysis and programming.”

The thinker’s toolkit: how to develop your critical thinking skills

Former CIA analyst and a critical thinking teacher, Morgan D. Jones authored a book called The thinker’s toolkit providing 14 techniques for problem solving. I have found the following techniques useful:

  • Problem restatement: when a solution to a problem seems infeasible, problem restatement can be helpful. Stating problems differently helps us bring the key assumptions to light. Sometimes the questions we ask have built-in biases, assumptions, and restrictions. For example, I know paranormal phenomena exists because I have experienced some things which can only be described as paranormal. Or an example from field that is hard to explain as well, fundraising: since we cannot print 100s of combinations of letters, we cannot send highly-segmented mailings. In this case, we can focus on the objective: sending highly-segmented mailings; and restate the problem: what are our options to send such mailings?
  • Divergent and convergent thinking: our survival methods and hence our evolution are partly responsible for ingroup bias or thinking. Observe any lines at a food court or a train station. People will usually gather around one or two spots. We believe that others’ decision must be sound and we follow that decision. (this is illustrated well in the first few minutes of Tina Seeling’s video. In contrast, this technique builds on the principle of “the more ideas, the better.” In the first stage, you generate as many ideas as possible without judgement, then you group the ideas together and select a few better ideas. [Do not try this at home, at least diverging. When my wife said nicely that my parents did not wish her happy birthday, I applied divergent thinking to explain why that must have happened. Mistake.]
  • Decision and event tree: when only ideas are discussed we take the risk of people judging those ideas. The decision and event tree reduce that risk. Imagine yourself in a situation where your group is discussing which project management software to use. This discussion could take any direction: from bringing out personal biases to wasting time. If you use a decision and event tree, however, you are more likely to stay focused on the issue at hand. In the project management software example, you can draw a tree with three branches for the three available software. Considering only one software at a time, under each branch, you can ask for pros and cons of every software. Once you have enough information on each software, you can make your decision.


In their book Analyzing Intelligence: Origins, Obstacles, and Innovations, George and Bruce describe a complete analyst. They posit that every analyst should possess, at a minimum, these seven characteristics:

  1. Mastery of the subject
  2. Understanding of research methods
  3. Imagination and rigor to generate and test hypotheses
  4. Understanding of intelligence collection methods
  5. Self-awareness of cognitive biases and influences
  6. Open-mindedness to contrary views
  7. Self-confidence to admit and learn from mistakes

George and Bruce argue that it is not the first three characteristics that separate good analysts from the best ones, but it is the last four: being imaginative and curious to explain and find missing information; being aware of self-biases; being a self-critic; and most importantly, being open to change her own mind.

Although the time of the decision-makers is limited, the complete analyst can actually persuade the decision-makers to use the recommendations of the analysis. Only then can we have truly data-driven decisions and help us move away from descriptive analysis to prescriptive synthesis.


Note: An edited version of this article was published in APRA‘s Connections magazine.

About the Author

A co-author of Data Science for Fundraising, an award winning keynote speaker, Ashutosh R. Nandeshwar is one of the few analytics professionals in the higher education industry who has developed analytical solutions for all stages of the student life cycle (from recruitment to giving). He enjoys speaking about the power of data, as well as ranting about data professionals who chase after “interesting” things. He earned his PhD/MS from West Virginia University and his BEng from Nagpur University, all in industrial engineering. Currently, he is leading the data science, reporting, and prospect development efforts at the University of Southern California.