Enhance Critical Thinking Skills in Data Analysis for Data-Driven Decision-Making

Think about the last time you heard someone admit to being wrong about something important and personal — not wrong about something insignificant, such as the scheduled time for an event, but rather wrong about a passionate belief or an ingrained personality trait. Can you think of any? If you can’t, that’s okay — these occurrences are rare. A person's identity is strongly associated with his or her personality and beliefs. Admitting to a fault or flaw in these areas is a challenge to the person's identity — not quite the person's existence but certainly his or her essence.

In an organization, leaders are often unwilling to admit their errors or uncertainties, because they are afraid that by doing so they will be perceived as weak or indecisive. If you follow politics, you can witness this phenomenon on both sides of the aisle — politicians who make decisions and embrace certain positions not because they believe it is best but because they are afraid of being perceived as weak or uncertain. They even go so far as to spin the facts to support their respective positions.

A University of California physicist named Richard Muller spent years arguing against global climate change. He helped found the group Berkeley Earth. Much of his work was funded by the gas and oil industry. Later, his own research found very strong evidence of global temperature increases associated with human activity. He concluded that he was wrong. Humans were to blame for climate change. Muller saw that the facts against is belief were too strong to ignore, so he changed his mind. He didn’t do it in a quiet way. He wrote a long op-ed piece in the New York Times that outlined his arguments and why the counter arguments were stronger.

The most effective leaders are actually those who are strong enough to admit when they are wrong. They are rational and make decisions based on information rather than opinion. They do not get defensive when challenged with facts that counter their assumptions.

Strong-Sense and Weak-Sense Critical Thinking

Challenging someone else's beliefs or assumptions is relatively easy compared to challenging one's own. The distinction can be attributed to two types of critical thinking:

• Strong-sense critical thinking: Asking questions that challenge your own beliefs or assumptions.
• Weak-sense critical thinking: Asking questions to pick apart someone else’s beliefs or assumptions.

There is no shortage of people who primarily engage in weak-sense critical thinking. They are the people who strongly defend their own positions and equally strongly attack the opposition. They're not very good at challenging their own positions or recognizing any merit in opposing positions. When losing an argument, they get defensive and emotional and often irrational because they so closely identify with the position they hold.

Strong-sense critical thinkers are rare. They are the people who, when confronted with information or opinions that contradict their beliefs or assumptions, are willing to listen to and explore other possibilities. They ask themselves, "Could I be wrong about this?" and "What if I am wrong about this?" They look at the facts, question their own assumptions, and pick apart the logic of their own reasoning. They are committed to the truth. These are the people you want on your data science team.

An Example

Imagine how different levels of data critical thinking might play out on a data science team. Suppose a running shoe website runs a promotion and sends out a coupon to everyone who buys a product. The data science team looks at the number of people who used the coupon to make a purchase, and the team produces the data visualizations shown below.

The graph on the left shows that more than half the customers received coupons, only about eight percent clicked on the coupon, and only about half of those people used the coupon.

The graph on the right compares coupon and no-coupon sales. Notice a few spikes in coupon sales primarily the day the coupon was issued and a few days afterward. Also notice that the coupon seems to have increased both coupon and non-coupon sales, but coupon sales account for a relatively small percentage (about 10 percent) of the total sales.

Your data science team wants to determine how successful the coupon was in increasing revenue. Of course, the team could simply look at total revenue in the 14 days prior to the coupon release and revenue in the 14 days after its release and compare the two numbers. However, that would shed light only on whether the coupon was effective and to what degree. It wouldn't explain why the coupon was effective or whether other, less costly, promotions would have been just as effective if not more so.

This is where strong-sense critical thinking comes into play. The data science team should be willing and able to ask more probing questions, such as the following:

• What did the revenue numbers look like during the same period last year? Is it possible that the boost in revenue was seasonal and unrelated to the coupon?
• Was the promotion worth the revenue lost to discounts? What was the return on investment (ROI). Was the coupon promotion worth it?
• What was the impact of the coupon on total sales per customer? Did it increase or decrease the average amount each customer spent?
• Did sales increase due to the coupon or simply the reminder? Would an inexpensive email reminder be just as effective? (This could be an interesting experiment.)

When your team applies strong-sense critical thinking, it should feel more like an open discussion. No one should feel as though they’re defending themselves. This approach is a great way for your team to ask interesting questions and in the end, gain greater insights.

Frequently Asked Questions

What is the role of critical thinking in data science?

The role of critical thinking in data science is essential for making accurate and informed decisions based on the data. It helps in evaluating data sources, identifying bias, and ensuring the reliability of the analysis.

How can I improve my critical thinking skills for data-driven decision-making?

To improve your critical thinking skills for data-driven decision-making, focus on developing your analytical skills, practice problem solving, and get more experience in analyzing data. Engaging with real-world data projects can also help in honing these skills.

Why is critical thinking important when analyzing data?

Critical thinking is important when analyzing data because it helps to uncover hidden patterns, identify anomalies, and make well-informed decisions. Good critical thinking skills ensure that you interpret data correctly and avoid drawing incorrect conclusions.

What are some strategies to develop strong critical thinking skills in data analysis?

Some strategies to develop strong critical thinking skills in data analysis include continuous learning, seeking feedback, collaborating with peers, and regularly questioning assumptions. Practicing these strategies can aid in honing your critical thinking ability.

How does critical thinking help in data-driven decision-making?

Critical thinking helps in data-driven decision-making by enabling you to systematically evaluate data, identify bias, and assess the veracity of information. This leads to more informed decisions based on the data rather than assumptions or incomplete information.

Can you provide some examples of critical thinking in data analysis?

Examples of critical thinking in data analysis include questioning the credibility of data sources, evaluating the methodologies used for data collection, identifying inconsistencies or biases in the data, and interpreting the results to make informed decisions.

How does data literacy contribute to better data-driven decisions?

Data literacy contributes to better data-driven decisions by equipping individuals with the knowledge to understand, interpret, and evaluate data accurately. It ensures that data is not misrepresented, and that decisions are made based on sound analysis skills.

What is critical thinking in data, and how can it be applied?

Critical thinking in data refers to the ability to assess and interpret data with a skeptical and analytical mindset. It can be applied by using critical thinking techniques such as identifying biases, verifying data sources, and cross-checking results to ensure accuracy.

What should I focus on to develop these skills for better data-driven decisions?

To develop these skills for better data-driven decisions, focus on enhancing your analytical skills, participating in data analysis exercises, and actively seeking opportunities to handle real-world data. Additionally, learning about common data biases and how to counter them is crucial.

Is critical thinking something that can be learned?

Absolutely, critical thinking is the ability that everyone can develop through practice and training. While it may not be something you acquire overnight, cultivating a habit of questioning assumptions, seeking evidence, and analyzing data critically will enhance your skills over time.

This is my weekly newsletter that I call The Deep End because I want to go deeper than results you’ll see from searches or AI, incorporating insights from the history of data and data science. Each week I’ll go deep to explain a topic that’s relevant to people who work with technology. I’ll be posting about artificial intelligence, data science, and data ethics.?

This newsletter is 100% human written ?? (* aside from a quick run through grammar and spell check).

More Sources:

1. https://www.sfmagazine.com/articles/2017/june/improving-critical-thinking-through-data-analysis/
2. https://www.dhirubhai.net/advice/0/how-can-critical-thinking-improve-data-zcawf
3. https://capabilitybuild.com/en/cursos/critical-thinking/data-driven-decision-making/
4. https://asana.com/resources/critical-thinking-skills
5. https://itb-academic-tests.org/en/2024/06/19/data-literacy-in-higher-education-an-important-ingredient-of-critical-thinking/
6. https://www.sfmagazine.com/articles/2017/june/improving-critical-thinking-through-data-analysis/