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Graduate Prerequisites: (CASCS111) (or equivalent), and at least one course in statistics. - Introduction to R, the computer language written by and for statisticians. Emphasis on data exploration, statistical analysis, problem solving, reproducibility, and multimedia delivery. Intended for MSSP and other graduate students. Effective Fall 2020, this course fulfills a single unit in the following BU Hub area: Critical Thinking.

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critical thinking . data science

The role of critical thinking in data science.

• Written by tpmunn
• Publish on September 29, 2022

*I frequently get asked questions about Data Science , so in the interest of helping as many people as possible, I’ve started this blog to answer those questions as simply as possible. This is a robust topic, and if you want a more in-depth discussion, please revisit my blog, where we will be going into greater depth at another time.

Critical thinking is the ability to think clearly and rationally while identifying and understanding logical connections between ideas. In data science, critical thinking is crucial to drawing actionable insights and improving business operations. Critical thinking also plays a significant role in data science, and you can learn how to improve your skills with these tips.

Critical thinking is a crucial skill in data science and analytics . Critical thinking is arguably one of data science’s most essential abilities. It strengthens their ability to dig deeper into information and extract the most meaningful insights.

In what ways does critical thinking impact data science? What makes it such a crucial skill for a data scientist ? Here is what you need to know.

What is Data Science?

The field of data science is continuously evolving and becoming a part of daily operations within various industries, from customer service to healthcare. Data science deals with large data sets and uses machine learning algorithms to detect patterns, pull insights, and make predictions.

By combining math, statistics, specialized programming, AI, and machine learning, data scientists can uncover actionable insights from a company’s data.

“These insights can indicate how to reach a target audience better, what products sell best, and how to enhance marketing efforts .”

This leads to more efficient, data-driven business decisions.

When working with data science, there’s a distinct lifecycle to the process, consisting of five stages, each with its specific tasks, including:

• Communicate

#1. Capture

This first stage is simply just about gathering raw data. The data can come from various sources, including websites and customer engagement analytics on social media. Often, this stage is also called data acquisition or data entry.

#2. Maintain

Critical thinking is vital here. You take the raw captured data and transform it into an efficient and accessible form. This stage is also known as data warehousing, data processing, or data cleansing because you clean up the data for practical applications.

#3. Process

Here, data scientists will take the data you organized and examine its patterns, ranges, and biases to determine how useful it will be in predictive analysis. This processing stage is also called data mining or data modeling.

#4. Analyze

This predictive analysis stage is the bulk of the data science lifecycle. It is where your data scientist will perform various analyses on the data, depending on what your goals for the data are.

#5. Communicate

Lastly, data scientists and analysts will prepare the analyses they completed and any findings from the data in an easily readable format (chart, graph, report, etc.). This is also called data visualization.

What is Critical Thinking?

Critical thinking is the ability to think clearly and rationally to understand the logical connections between ideas. Critical thinking isn’t a novel concept, as it has been around since the days of ancient Greek philosophers like Socrates and Plato.

Critical thinking can also be described as engaging in reflective and independent thinking while being an active learner rather than passively receiving information. Someone skilled in critical thinking often questions ideas and assumptions rather than accepting what they’re told at face value.

Critical thinkers are also much more systematic and analytical in problem-solving. They aren’t ones to act on intuition or instinct alone. Necessary thinking skills are instrumental on the job, whether working in an entry-level position or as a top executive.

“Good critical thinking skills allow you to objectively analyze the facts to solve problems, yielding more sound and effective decision-making.”

Whether you’re at work or in another setting, there are general steps to take when using critical thinking to problem-solve, including:

• Identify the Problem
• Determine Why the Problem Exists and How it Can Be Solved
• Research the Issue and Collect Data or Information
• Organize the Data and Findings
• Develop and Execute Solutions
• Analyze Which Solutions Worked and Which Didn’t
• Identify Any Ways to Improve

Critical thinking is generally a broad term. Professional data scientists also have these necessary thinking skills. They can objectively analyze data and draw thoughtful insights. There are many critical thinking skills to tap into, but six of the most essential skills to the necessary thinking process include:

• Identifying Biases
• Identification
• Judging Relevance

#1. Identifying Biases

Critical thinking is supposed to be objective and fact-driven, so it’s crucial to identify when you or others have a cognitive bias. It’s also vital to determine when the data or information you’re analyzing may also be biased.

Biases can influence how you understand and respond to information presented to you. However, critical thinking encourages you to question yourself and consider those alternate points of view you may have. Identifying biases can be helpful when analyzing company data, deciding what advertisement to run, and even making hiring decisions.

#2. Inference

This is the ability to draw conclusions based on your given information. Without the ability to form the findings, it’d be hard to act after analyzing facts and data. Creating inferences is all about processing information and is a big part of being good at critical thinking.

#3. Research

Because critical thinking is objective and analytical, you need solid research skills to discover the facts and figures required to make your decisions or arguments. Every situation won’t require you to research the problem and potential solutions. However, strong research skills and deciphering information sources can ensure you gather the right stuff.

#4. Identification

While it’s a little similar to researching and forming inferences, the identification skill is more about being able to identify problems as well as what may be influencing that problem. Without the identification skill, you likely won’t know when you’re in a situation where it may be beneficial to think critically. It’s also hard to solve a problem when you can’t identify what caused it in the first place.

#5. Curiosity

Critical thinking is about questioning everything, and curiosity plays a significant role. While some people are naturally curious, it’s also a skill that can be learned. To practice curiosity and build up your skill, try approaching all situations with a “beginner’s mindset,” meaning you’re brand new to the case and know nothing about it. You’re there to learn with an open mind. This allows you to absorb further information and perceive things you likely didn’t notice before.

#6. Judging Relevance

Throughout the critical thinking process, you’ll likely encounter loads of information when trying to solve problems. Still, not all of it will be relevant to your specific goals. This is where the skill of judging relevance comes in.

For example, when researching a topic online, Google will give you thousands of search results on just about everything that has to do with an issue. However, you won’t use all or even half of it. You’re constantly judging the relevance of the information presented to you to determine what’s valuable. Without this skill, you’d be wasting time on irrelevant details that prevent you from concluding.

Critical thinking is a worthwhile skill for any professional to master. Yet, it’s precious to data scientists working in the data science field. Critical thinking can have varying applications depending on the industry you’re in. There are essentially two aspects to learn:

• Developing a Question
• Questioning the Data

#1. Developing a Question

When you begin pursuing data science and set goals for gathering actionable insights, determine what question you want to answer or what problem you’re looking to solve. For example, the question could be, “ How do we better reach our customers on social media ?”

The ability to develop a question will likely require extensive research and interviewing to grasp what problems you’re hoping to solve. It’s essentially a collaboration between data scientists who thoroughly understand the data and business owners who thoroughly understand the business goals.

It takes solid critical thinking skills to develop a suitable question to test.

“The data scientist needs to be able to sift through the information while judging relevance when presenting a company’s goals.”

Critical thinking is like developing a scientific hypothesis. It needs to be testable, applicable, and efficient to work, and it takes strong skills to make that determination.

#2. Questioning the Data

The second aspect of data science critical thinking is questioning the data. This is where many of the excellent essential thinking skills come into play. Experienced data scientists don’t dump data into spreadsheets or software and wait to see what the output is. Professional data scientists with strong critical thinking skills will analyze the data to discover adjustments, misleading errors, biases, or other distracting factors.

The ability to question data ensures that the data and insights gathered are the best they can be. Without challenging the data and thoroughly evaluating it, the insights you pull could be ineffective or false. Biases, input errors, and general holes in the data can all be vetted during the questioning process.

Missing this crucial step can result in embarrassing moments for your company. For example, suppose your data team shares insights on data they didn’t question. In that case, they could disclose incorrect information that jeopardizes your company’s reputation. Or these poor insights could lead you to spend money on projects that aren’t guaranteed to have the expected results.

Critical Thinking, the Most In-Demand Data Science Skill

In terms of the soft, non-technical skills needed to be successful in data science, critical thinking is often at the top of the list. Critical thinking can lead to better, well-informed, more precise decisions based on facts.

“Businesses need data scientists who can appropriately frame questions and understand the results they’re gathering to turn their findings into actions.”

Strong critical thinking skills allow you to see all angles of a problem. It is precious to business owners looking to level up their data usage and make more data-driven decisions.

When looking for data scientists , business owners will be looking for people who can maintain a detailed understanding of the business and its goals. They favor objectively analyzed data sets with the business’s best interests. This is becoming especially important in big data, as countless businesses are looking to tap into their data sources for insights.

Critical thinking skills are going to come into play here. Much like curiosity, some people are natural critical thinkers. However, it’s also a skill that can be learned and mastered over time by employing these practices:

• Asking Basic Questions
• Questioning Assumptions
• Being Aware of Your Mental Processes
• Try Reversing Problems
• Evaluate Evidence
• Think Independently

#1. Asking Basic Questions

When you’re deep in the trenches of critical thinking or data analysis, things can get pretty complex, and you can lose sight of your original question.

To avoid this, ask yourself basic questions like, what do you already know, and how do you know that? What are you overlooking? This will help you reframe the original question or problem you set out to solve and keep you on your toes.

#2. Questioning Assumptions

When building up your critical thinking skills, it’s important not to get complacent with the information around you. Just because something has always been one way doesn’t necessarily mean that’s the best way.

To generate new insights and dig deeper into the information around you, start questioning your basic assumptions about the world. This may be where you make new, innovative discoveries by examining the information already out there.

#3. Being Aware of Your Mental Processes

This is where your cognitive biases come into effect. Everyone has biases in their thinking. It’s a natural part of being human. However, being objective is a massive part when trying to think critically. So, taking stock of your biases and how they may influence decisions is essential.

#4. Try Reversing Problems

Sometimes, you may get stuck on a problem after you’ve spent ample time on it. Situations like this can be frustrating. However, you may be able to reposition the problem by reversing it. For example, say you’re trying to determine why one thing causes another to happen, and you can’t seem to figure it out. Try reversing the factors to see if that helps rewire your thought process and point you toward a solution.

#5. Evaluate Evidence

When setting out to solve a problem, evaluating existing evidence to see what work has already been done in the area can be helpful. It removes some workloads and helps you learn how to problem-solve efficiently.

#6. Think Independently

While researching and analyzing data and information is a big part of critical thinking, it’s crucial to remember to still think for yourself.

Essential Skills for Data Scientists

As technology continues advancing, the demand for experienced workers in the data science field is going to increase. According to the U.S. Bureau of Labor Statistics , data scientist employment is expected to grow by 35% from 2022 to 2032, which is significantly faster than any other occupation.

It’s a great time to be a data scientist or to explore and enter the field.

Education is a big part of data science. Still, outside of the academic curriculum, there are several other practical skills you should practice and develop that can set you apart from the competition of data scientists looking to cash in on the influx of data science jobs in the coming years.

These essential skills fall under both the technical and non-technical umbrella. You typically develop technical skills in academic courses or formal business training. Technical skills are likely the ones you would list at the top of your resume and the ones you’d see listed at the beginning of the job description, as these are skills that businesses put a lot of emphasis on.

Non-technical skills require less technical, academic training or a formal certification. Nonetheless, these foundational skills help you excel as a data scientist. No matter how much specialized training or academic accreditations you may have, you won’t thrive or reach your full potential in the data science field without these non-technical skills. And one of the top non-technical skills you need in the data science field is, in fact, critical thinking.

Critical thinking is crucial to uncovering insights, appropriately framing questions, and understanding how data relates to the business and its overarching goals. In data science, critical thinking skills allow you to see all angles of a problem. What’s more, critical thinking is a skill that is so valuable it can quickly transfer to any field or profession and be wildly helpful, as nearly all careers require some level of critical thinking to be successful.

Along with critical thinking, some other essential skills — both technical and non-technical — for data scientists to have include:

• Effective Communication
• Problem-Solving
• Business Sense
• Ability to Prepare Data for Analysis
• Math and Statistics
• Ability to Work With Machine Learning and Artificial Intelligence

#1 Effective Communication

Good communication skills are helpful in any field. Still, they’re especially important in data science because you need to be able to effectively communicate what the insights you pull from data mean in terms that are relevant to the business and highlight all the ways you can use them to reach the organization’s set goals.

Communication skills are vital to data scientists because they need to communicate the findings from their data analysis straightforwardly to both technical and non-technical audiences — not everyone will have the background knowledge to decipher the meaning of data science jargon.

Data scientists with effective communication skills can help promote data literacy throughout the business as they work to ensure all organization members understand how data works and how it can impact operations.

#2 Problem-Solving

Successful data scientists have proactive problem-solving skills. They know how to dig into the root causes of issues that arise in your organization and develop actionable solutions to problems.

A desire to solve problems is almost a requirement for data scientists as that’s really what their job is all about. Data scientists are setting out to uncover opportunities to analyze and explain problems in a business and then identify potential solutions by pulling insights from data that explain why these problems exist.

#3 Curiosity

It pays to be curious, especially as a data scientist. In data science, you’re constantly on the prowl for answers and always asking, “Why?”

You should also always dive deeper than the surface in your quest for answers. Of course, you want to answer the initial questions the data presents. Still, you should also look deeper to uncover information that may have never come up in your original data assessment.

A strong sense of intellectual curiosity is a non-technical skill that can set you apart as a data scientist, and it’s often in these deeper dives for information that the real valuable insights come about.

#4 Business Sense

Another non-technical skill that’s crucial for data scientists to have is a strong sense of the business they’re working with. As a data scientist, you must thoroughly understand the company and the field you’re working in to accurately interpret the data you’re analyzing and generate insights and solutions relevant to the business.

Data science is a lot of number crunching, but it isn’t just number crunching. Without a sense of the business, the insights you gather from data may not be helpful as you won’t have the background knowledge to use them to foster growth and future success.

It will likely take time to develop this skill as you need to immerse yourself in the business and its culture to know what problems must be solved and how data can act as a solution.

#5 Ability to Prepare Data for Analysis

Now, switching gears to the necessary technical skills for data scientists, data preparation is preparing data for analysis. This will include data discovery, translation, and cleaning — all paramount to a successful workflow.

Data scientists can use various data preparation tools to streamline the process for beginners and experts. Data preparation is a topic that’s typically taught in an academic setting or through a certification program. Data scientists will learn to analyze large volumes of data, both structured and unstructured, as well as best practices for presenting findings in a way that all members of the organization can understand.

A data scientist’s job primarily exists deep in the trenches of complex algorithms and analytical systems, so it’s essential to understand the inner workings of these systems. This is where a solid education in coding comes in handy.

Several different coding languages are used in data science, each with unique applications. Some of these include:

Some data scientists may choose to be experts in one coding language, while others may have training in multiple languages. Regardless of your coding career path, it’s a critical skill to have and you should take time to learn the coding language most relevant to your business, role, and data-related challenge.

#7 Math and Statistics

Like coding, a thorough education in mathematics and statistics is essential for data scientists. Data scientists regularly work with statistical and mathematical models. Understanding these two subject areas helps you think critically about the value of various data points and what questions the data can — and can’t — answer.

Rigorous statistical thinking allows you to see through the noise in a data set to extract value and meaning and identify meaningful patterns and connections between the data. This skill is critical as it gives the data purpose within the organization you’re working for.

#8 Ability to Work with Machine Learning and Artificial Intelligence

Machine learning and artificial intelligence (AI) are two precious tools that will enhance the value of your work as a data scientist while helping you complete your work quicker and with more quality and efficiency.

Learning about machine learning and AI in an educational or other technical setting will teach you how to know when you have the correct data for an AI model and how to train and deploy those models to derive actionable insights from data.

Of course, AI and machine learning education are valuable in data science, but because the fields are growing at such a significant pace, AI and machine learning can be helpful across multiple industries.

According to research from Exploding Topics , the global AI market is valued at $136 billion, making it an extremely profitable industry to get into. Moreover, that value is expected to increase 13 times over the next seven years.

The research also shows that, by 2025, 97 million people will work in the AI sector in some form, indicating an abundance of opportunity for those looking to become data scientists or hold similar positions in the space.

Plus, major companies are already tapping into data through AI and machine learning and seeing massive ROIs. Take Netflix , for example. The giant streaming platform uses AI to fuel its automated, highly personalized recommendation system that recommends content to users based on their likes and dislikes, interests and watch history. This system alone produces $1 billion yearly for the company as value through customer retention.

As a data scientist equipped with critical thinking skills — and all the other essential skill sets for the field — this industry growth and job prospects should be very motivating.

Critical thinking is a crucial skill in data science and analytics. Critical thinking is arguably one of the most essential skills a data scientist can offer. It strengthens their ability to dig deeper into the data to extract meaningful insights.

Critical thinking is a worthwhile skill for any professional to master. However, it’s precious to those in the data science field. Critical thinking can lead to better, well-informed, more precise decisions based on facts and data. It is a fundamental skill for those working in data science to add to their resume.

Tiffany Perkins-Munn orchestrates aggressive strategies to identify objectives, expose patterns, and implement game-changing solutions with the agility that transcends traditional marketing. As the Head of Data and Analytics for the innovative CDAO organization at J.P. Morgan Chase, her knack involves unraveling complex business problems through operational enhancements, augmented financials, and intuitive recruiting. After over two decades in the industry, she consistently forges robust relationships across the corporate spectrum, becoming one of the Top 10 Finalists in the Merrill Lynch Global Markets Innovation Program.

Dr. Perkins-Munn earned her Ph.D. in Social-Personality Psychology with an interdisciplinary focus on Advanced Quantitative Methods. Her insights are the subject of countless lectures on psychology, statistics, and real-world applications. As a published author, coursework developer, and Dissertation Committee Chair Tiffany still finds time for family and hobbies. Her non-linear career path has given her an exclusive skill set that is virtually impossible to reproduce in another individual.

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Critical thinking: A must-have skill in data analytics

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In 2020, the Data Science Council of America reported that 89% of CEOs believed they would lose their market share without insights from big data. Yet 45% of CEOs said inadequate data insights were hindering their customer insights and 56% didn’t feel they could rely on the validity of their data.

Behind this mistrust are questions about the accuracy of analytics software — but is it also time to consider that employees themselves might lack some of the critical thinking  skills needed to get the most out of their analytics?

The Brookings Institution offers an example of automated analytics risk assessments that have been used by US judges to determine bail and sentencing limits. These analytics “can generate incorrect conclusions, resulting in large cumulative effects on certain groups, like longer prison sentences or higher bails imposed on people of color,” Brookings said. The reason is that historically, certain groups of people have been subjected  to more frequent and harsher sentencing.

In another example, RAND Corporation reported that COVID-19 recommendations for social distancing were initially based upon data collected from smart thermometers, but this data didn’t  necessarily take into account that it would most likely be healthier and wealthier people who had the thermometers.

Both cases would have benefitted from more critical thinking — and it is exactly “false positive” analytics reports like this that generate distrust of analytics in CEOs.

SEE: Artificial Intelligence Ethics Policy (TechRepublic Premium)

How can companies improve the critical thinking of employees and their analytics?

1. Emphasize critical thinking skills

“ Modern students are quite adept at memorizing and regurgitating facts presented in class or in reading materials, but the ability to reason, think critically, and problem-solve has actually been dramatically reduced in recent years,” said Stephen Camarata, Ph.D., a professor at both the Bill Wilkerson Center and the Vanderbilt University School of Medicine. Potential causative factors cited are parents not putting enough time into developing their children’s critical thinking and college professors making classes easier so they can receive better instructor and course evaluations from students.

Regardless of cause, it’s clear that business must take an active role in finding and developing employees with critical thinking skills. One way this can be done is by teaming with local colleges and universities in the development of curricula and student internship programs that place emphasis on critical thinking skills , such as correct problem identification, research, bias detection and inference of conclusions based upon analyses of raw information.

2. Recruit for critical thinking

HR departments can aid in the process of critical thinking hiring by administering pre-employment critical thinking and problem-solving aptitude tests .

3. Develop and embed critical thinking skills in your organization

IT, data science and other internal departments can further critical thinking by embedding it into analytics methodology. For example, an analytics QA checklist could include steps like:

• Have you assessed the analytics for potential bias?
• Have you reviewed all possible data sources to ensure that the analytics are being applied to the most inclusive set of data?
• Is there anything about the subject being studied that might have been missed?
• Is there anyone else inside or outside of the organization who should participate in the QA review?

Final words

There is pressure in companies for individuals to perform, and to do it quickly. At the same time, there are pressures on employees to align their opinions with prevailing thought. If the goal of analytics is to create breakthroughs in insights, analytics must be free to break through conventional thinking, so uncommon and elusive problems can be solved.

The Indian teacher Sadhguru once said,  “When your mind is full of assumptions, conclusions, and beliefs, it has no penetration, it just repeats past impressions.”

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5 Essential Business-Oriented Critical Thinking Skills for Data Science

Data science requires a range of sophisticated technical skills. Don’t let that expertise get in the way of critical thinking, though, or you could end up doing more harm than good for your business partners.

As Alexander Pope said, to err is human. By that metric, who is more human than us data scientists? We devise wrong hypotheses constantly and then spend time working on them just to find out how wrong we were.

When looking at mistakes from an experiment, a data scientist needs to be critical, always on the lookout for something that others may have missed. But sometimes, in our day-to-day routine, we can easily get lost in little details. When this happens, we often fail to look at the overall picture, ultimately failing to deliver what the business wants.

Our business partners have hired us to generate value. We won’t be able to generate that value unless we develop business-oriented critical thinking, including having a more holistic perspective of the business at hand. So here is some practical advice for your day-to-day work as a data scientist. These recommendations will help you to be more diligent and more impactful at the same time.

1. Beware of Clean Data Syndrome

Tell me how many times this has happened to you: You get a data set and start working on it straight away. You create neat visualizations and start building models. Maybe you even present automatically generated descriptive analytics to your business counterparts!

But do you ever ask, “Does this data actually make sense?” Incorrectly assuming that the data is clean could lead you toward very wrong hypotheses. Not only that, but you’re also missing an important analytical opportunity with this assumption.

You can actually discern a lot of important patterns by looking at discrepancies in the data. For example, if you notice that a particular column has more than 50 percent of values missing, you might think about dropping the column. But what if the missing column is because the data collection instrument has some error? By calling attention to this, you could have helped the business to improve its processes.

Or what if you’re given a distribution of customers that shows a ratio of 90 percent men versus 10 percent women, but the business is a cosmetics company that predominantly markets its products to women? You could assume you have clean data and show the results as is, or you can use common sense and ask the business partner if the labels are switched.

Such errors are widespread. Catching them not only helps the future data collection processes, but also prevents the company from making wrong decisions by preventing various other teams from using bad data.

2. Be Aware of the Business

You probably know fab.com. If you don’t, it’s a website that sells selected health and fitness items.  But the site’s origins weren’t in e-commerce. Fab.com started as Fabulis.com, a social networking site for gay men. One of the site’s most popular features was called the “Gay Deal of the Day.”

One day, the deal was for hamburgers. Half of the deal’s buyers were women, despite the fact that they weren’t the site’s target users. This fact caused the data team to realize that they had an untapped market for selling goods to women. So Fabulis.com changed its business model to serve this newfound market.

Be on the lookout for something out of the ordinary. Be ready to ask questions. If you see something in the data, you may have hit gold. Data can help a business to optimize revenue, but sometimes it has the power to change the direction of the company as well.

Another famous example of this is  Flickr, which started out as a multiplayer game . Only when the founders noticed that people were using it as a photo upload service did the company pivot to the photo sharing app we know it as today.

Try to see patterns that others would miss. Do you see a discrepancy in some buying patterns or maybe something you can ’ t seem to explain? That might be an opportunity in disguise when you look through a wider lens.

3. Focus on the Right Metrics

What do we want to optimize for? Most businesses fail to answer this simple question.

Every business problem is a little different and should, therefore, be optimized differently. For example, a website owner might ask you to optimize for daily active users. Daily active users is a metric defined as the number of people who open a product in a given day. But is that the  right metric ? Probably not! In reality, it’s just a vanity metric, meaning one that makes you look good but doesn’t serve any purpose when it comes to actionability. This metric will always increase if you are spending marketing dollars across various channels to bring more and more customers to your site.

Instead, I would recommend optimizing the percentage of users that are active to get a better idea of how my product is performing. A big marketing campaign might bring a lot of users to my site, but if only a few of them convert to active, the marketing campaign was a failure and my site stickiness factor is very low. You can measure the stickiness by the second metric and not the first one. If the percentage of active users is increasing, that must mean that they like my website.

Another example of looking at the wrong metric happens when we create classification models. We often try to increase accuracy for such models. But do we really want accuracy as a metric of our model performance?

Imagine that we’re predicting the number of asteroids that will hit the Earth. If we want to optimize for accuracy, we can just say zero all the time, and we will be 99.99 percent accurate. That 0.01 percent error could be hugely impactful, though. What if that 0.01 percent is a planet-killing-sized asteroid? A model can be reasonably accurate but not at all valuable. A better metric would be the F score, which would be zero in this case, because the recall of such a model is zero as it never predicts an asteroid hitting the Earth.

When it comes to data science, designing a project and the metrics we want to use for evaluation is much more important than modeling itself. The metrics themselves need to specify the business goal and aiming for a wrong goal effectively destroys the whole purpose of modeling. For example, F1 or PRAUC is a better metric in terms of asteroid prediction as they take into consideration both the precision and recall of the model. If we optimize for accuracy, our whole modeling effort could just be in vain.

4. Statistics Lie Sometimes

Be skeptical of any statistics that get quoted to you. Statistics have been  used to lie in advertisements, in workplaces, and in a lot of other arenas in the past. People will do anything to get sales or promotions.

For example,  do you remember Colgate’s claim that 80 percent of dentists recommended their brand? This statistic seems pretty good at first. If so many dentists use Colgate, I should too, right? It turns out that during the survey, the dentists could choose multiple brands rather than just one. So other brands could be just as popular as Colgate.

Marketing departments are just myth creation machines. We often see such examples in our daily lives. Take, for example, this 1992 ad from Chevrolet . Just looking at just the graph and not at the axis labels, it looks like Nissan/Datsun must be dreadful truck manufacturers. In fact, the graph indicates that more than 95 percent of the Nissan and Datsun trucks sold in the previous 10 years were still running. And the small difference might just be due to sample sizes and the types of trucks sold by each of the companies. As a general rule, n e ver trust a chart that doesn’t label the Y-axis.

As a part of the ongoing pandemic, we’re seeing even more such examples with a lot of studies promoting cures for COVID-19. This past June in India, a man  claimed to have made a medicine for coronavirus that cured 100 percent of patients in seven days. This news predictably caused a big stir, but only after he was asked about the sample size did we understand what was actually happening here. With a sample size of 100, the claim was utterly ridiculous on its face. Worse, the way the sample was selected was hugely flawed. His organization selected asymptomatic and mildly symptomatic users with a mean age between 35 and 45 with no pre-existing conditions, I was dumbfounded — this was not even a random sample. So not only was the study useless, it was actually unethical.

When you see charts and statistics, remember to evaluate them carefully. Make sure the statistics were sampled correctly and are being used in an ethical, honest way.

5. Don’t Give in to Fallacies

During the summer of 1913 in a casino in Monaco, gamblers watched in amazement as the roulette wheel landed on black an astonishing 26 times in a row. And since the  probability of red versus black is precisely half, they were confident that red was “due.” It was a field day for the casino  and  a perfect example of  gambler’s fallacy , a.k.a. the Monte Carlo fallacy.

This happens in everyday life outside of casinos too.  People tend to avoid long strings of the same answer . Sometimes they do so while sacrificing accuracy of judgment for the sake of getting a pattern of decisions that look fairer or more probable. For example, an admissions office may reject the next application they see if they have approved three applications in a row, even if the application should have been accepted on merit.

The world works on probabilities. We are seven billion people, each doing an event every second of our lives. Because of that sheer volume, rare events are bound to happen. But we shouldn’t put our money on them.

Think also of the spurious correlations we end up seeing regularly. This particular graph shows that organic food sales cause autism. Or is it the opposite? Just because two variables move together in tandem doesn’t necessarily mean that one causes the other. Correlation does not imply causation and as data scientists, it is our job to be on a lookout for such fallacies, biases, and spurious correlations. We can’t allow oversimplified conclusions to cloud our work.

Data scientists have a big role to play in any organization. A good data scientist must be both technical as well as business-driven to perform the job’s requirements well. Thus, we need to make a conscious effort to understand the business ’  needs while also polishing our technical skills.

More From Rahul Agarwal Don’t Democratize Data Science

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CONCEPTUAL ANALYSIS article

Key information processes for thinking critically in data-rich environments.

• Centre for Research in Applied Measurement and Evaluation, University of Alberta, Edmonton, AB, Canada

The objective of the present paper is to propose a refined conception of critical thinking in data-rich environments. The rationale for refining critical thinking stems from the need to identify specific information processes that direct the suspension of prior beliefs and activate broader interpretations of data. Established definitions of critical thinking, many of them originating in philosophy, do not include such processes. A refinement of critical thinking in the digital age is developed by integrating two of the most relevant areas of research for this purpose: First, the tripartite model of critical thinking is used to outline proactive and reactive information processes in data-rich environments. Second, a new assessment framework is used to illustrate how educational interventions and assessments can be used to incorporate processes outlined in the tripartite model, thus providing a defensible conceptual foundation for inferences about higher-level thinking in data-rich environments. Third, recommendations are provided for how a performance-based teaching and assessment module of critical thinking can be designed.

Introduction

In response to the question, how much data are on the internet, Gareth Mitchell from Science Focus Magazine answers the question by considering the overall data held by just four companies - Amazon, Facebook, Google, and Microsoft ( https://www.sciencefocus.com/future-technology/how-much-data-is-on-the-internet/ ). These four companies are estimated to hold a sum total of at least 1,200 petabytes (PB) of online data, which equals 1.2 million terabytes (TB) or 1.2 trillion gigabytes (GB). Neuroscientists propose that the average human brain holds 2.5 PB or 2.5 million GB of information in memory ( Reber, 2010 ), or just over 7 billion 60,000-word books. However, information stored in memory is often subject to error not only from the way it is encoded but also retrieved ( Mullet and Marsh, 2016 ).

Critical thinking requires people to minimize bias and error in information processing. Students entering post-secondary education today may be “digital natives” ( Prensky, 2001 ) but they are still surprisingly naïve about how to critically think about the wealth of digital information available. According to Ridsdale et al. (2015) , youth may be quite adept at using digital hardware such as smart phones and apps but they often lack the mindware to think and act critically with the information they access with their devices ( Stanovich, 2012 ). Although this lack of mindware can be observed in the mundane activities of how some first-year undergraduates might tackle their research assignments, it is dramatically illustrated in the political narratives of radicalized young adults ( Alava et al., 2017 ). Young adults are particularly vulnerable to misinformation because they are in the process of developing their cognitive abilities and identities ( Boyd, 2014 ). The objective or rationale for this paper is to propose a refined conception ( Ennis, 2016 ) of critical thinking in data-rich environments. It is the authors’ view that a refined conception is required because data-rich environments have ushered in many cognitive traps and the potential for personal biases to derail critical thinking as traditional understood. The research questions addressed in this conceptual paper are as follows: What can traditional definitions of critical thinking gain by considering explicit inclusion of cognitive biases? How can refined definitions of critical thinking be incorporated into theoretical frameworks for the design of performance assessments?

One of the most recommended strategies for helping young adults analyze and navigate online information is to directly and explicitly teach and assess critical thinking ( Alava et al., 2017 ; Shavelson et al., 2019 ). However, teaching and assessing critical thinking is fraught with difficulties, including a multitude of definitions, improper evaluation, and studies that incorporate small samples and controls ( Behar-Horenstein and Niu, 2011 ; El Soufi and Huat See, 2019 ). Aside from these predictable difficulties, new challenges have emerged. For example, the informational landscape has changed over the course of the last 30 years. The rapid increase in quantity coupled with the decrease in quality of much online information challenges the limits of human information processing.

Critical thinking today is primarily conducted in data-rich online environments, meaning that postsecondary students are searching, navigating, and thinking about a virtually limitless number of sources. Oxford University’s Change Data Lab ( Roser et al., 2020 ) writes: “adults aged 18–29 in the US are more likely to get news indirectly via social media than directly from print newspapers on news sites; and they also report being online ‘almost constantly.’” As shown in Figure 1 , not only is the total time spent online increasing but the increase is mostly the time spent on mobile phones. As mobile phones are smaller devices, compared to desktops, laptops, and tablets, they can be expected to force even faster navigation and processing of information, which would be expected to increase the odds of error-prone thinking.

FIGURE 1 . Max Roser, Hannah Ritchie, and Esteban Ortiz-Ospina (2020) - Internet . Published online at OurWorldInData.org . Retrieved from: https://ourworldindata.org/internet [Online Resource]; Data source accessed https://www.bondcap.com/report/itr19/ . Permission granted under the common creative license.

Cognitive traps are ubiquitous in online data-rich environments. For example, information can be presented as serious and credible when it is not. However, traditional critical thinking definitions have not tended to focus on avoiding cognitive traps; namely, how processing errors can be avoided. This creates a problem not only for teaching but also assessing critical thinking among postsecondary students in today’s classrooms. Thus, there are at least two research opportunities in addressing this problem: 1) provide a refinement of what critical thinking entails specifically for the teaching and assessment of critical thinking in data-rich environments and 2) illustrate a framework for the design of teaching and assessment modules that can lead to stronger inferences about students’ critical thinking skills in today’s information world.

The present paper contributes to the literature on critical thinking in data-rich environments by providing a refinement of what critical thinking entails for teaching and assessment in data-rich environments. The refinement is rooted in cognitive scientific advancements, both theoretical and empirical, of higher-level thinking, and essentially attempts to offer test designers an update on the construct of critical thinking. In other words, this conceptual analysis does the work of translating key psychological aspects of the critical thinking construct for pragmatic purposes–student assessment. Building on the refinement of this construct, the paper also includes recommendations for the type of framework that should guide the design of teaching and assessment modules so that key aspects of students’ critical thinking skills are not missed. Toward this end, this refinement can enhance the construct representation of assessments of critical thinking in data-rich environments. Educational assessments are only as good as their representation of the construct intended for measurement. Without the ongoing refinement of test constructs such as critical thinking, assessments will not provide the most accurate information in the generation of inferences of student thought; refinements of test constructs are especially vital in complex informational landscapes ( Leighton and Gierl, 2007 ). Thus, a refinement of critical thinking among young adults in data-rich environments is developed by integrating two of the most topical and relevant areas of research for this purpose: First, Stanovich and Stanovich’s (2010) tripartite model of critical thinking is used to outline the limitations of human information processing systems in data-rich environments. Second, Shavelson et al.’s (2019) assessment framework is used to illustrate how specific educational assessment designs can be built on the tripartite model and can provide a more defensible evidentiary base for teaching and drawing inferences about critical thinking in data-rich environments. The paper concludes with an illustration of how mindware can be better integrated into teaching and performance-based assessments of critical thinking. The present paper contributes directly to the special issue on Assessing Information Processing and Online Reasoning as a Prerequisite for Learning in Higher Education by refining the conceptualization of critical thinking in data-rich environments among postsecondary students. This refinement provides an opportunity to guide instructive and performance-based assessment programs in the digital age.

Theoretical Frameworks Underlying Mindware for Critical Thinking

In the 1999 science fiction movie Matrix Wachowski et al. (1999) , human beings download computer “programs” to allow them to think and function in a world that has been overtaken by intelligent machines. Not only do these programs allow human beings to live in a dream world, which normalizes a dystopian reality, but also to effortlessly disregard their colonization. Cognitive scientists propose something analogous to these “programs” for human information processing. For example, Perkins (1995) coined the term mindware to refer to information processes, knowledge structures, and attitudes that can be acquired through instruction to foster good thinking. Rizeq et al. (2020 , p. 2) indicate contaminated mindware as “beliefs that may be unhelpful and that may inhibit reasoning processes … ( Stanovich, 2009 ; Stanovich et al., 2008 ; Stanovich, 2016 ).”

Treating human information processing as analogous to computer programs, which can be contaminated, is useful and powerful because it highlights the presence of errors or bugs in thinking that can invariably distort the way in which data are perceived and understood, and instantaneously “infect” the thinking of both self and others. However, the predictability of such programs also permits anticipating when these thinking errors are likely to occur. Educational interventions and assessments can be designed to capitalize on the predictability of thinking errors to provide a more comprehensive level of thinking instruction and evaluation. Specifying what critical thinking entails in data-rich environments requires explicit attention not only to the information processes, knowledge structures, and attitudes that instantiate good critical thinking but also to the thinking bugs that derail it. Hyytinen et al. (2019 , p. 76) indicate that a critical thinker needs to have knowledge of what is reasonable, the thinking skills to evaluate and use that knowledge, as well as dispositions to do so (Facione, 1990; Halpern, 2014; Hyytinen et al., 2015).” We agree but we would go further in so far as critical thinkers also need to know what their own biases are and how to avoid cognitive traps ( Toplak and Flora, 2020 ).

Traditional Definitions of Critical Thinking

Established or traditional definitions of critical thinking have typically focused on the proactive processes that comprise critical thinking ( Leighton, 2011 ). Proactive processes are positive in action. Proactive processes, such as analyzing and evaluating, are often the focus of educational objectives (e.g., Bloom’s taxonomy; Bloom, 1956 ). Proactive processes help to identify the actions and goals of good thinking in ideal or optimal conditions. However, they are not particularly useful for creating interventions or assessments intended to diagnose faulty thinking ( Leighton and Gierl, 2007 ). The problem is that these processes reflect only aspects of good thinking and do not reflect other processes that should be avoided for good thinking to occur. For example, reactive thinking processes such as neglecting and confirming must be resisted in order for proactive processes do their good work. Reactive processes are not bad in many circumstances, especially those where thinking has to be quick to avoid imminent danger ( Kahneman, 2011 ). However, in circumstances where imminent danger is not present and actions can be enhanced by careful processing of information, it can be useful to learn about reactive processes; this is especially relevant for designing teaching interventions and assessments of critical thinking ( Leighton, 2011 ).

The omission of reactive processes in traditional definitions of critical thinking is perhaps not surprising since many of these definitions grew out of philosophy and not out of empirical disciplines such as experimental psychology ( Ennis, 2015 , Ennis, 2016 ). Nonetheless, this section addresses established definitions in order to provide a conceptual foundation on which to build more, targeted definitions of critical thinking for specific purposes.

Proactive Processes in Critical Thinking

Ennis (2016) provides a justification for distinguishing the basic concept of critical thinking from a particular conception of it; that is, a particular definitional instance of it in specific situations. In an analysis of the many theoretically inspired definitions of critical thinking, Ennis (2016 , p. 8) explains that many established definitions share a conceptual core. To illustrate this core, consider three definitions of critical thinking outlined in Ennis (2016 , p.8-9):

1. “Active, persistent, and careful consideration of any belief or supposed form of knowledge in the light of the grounds that support it and the further conclusions to which it tends” ( Dewey, 1933 , p. 9 [first edition 1910]).

2. “Purposeful, self-regulatory judgment which results in interpretation, analysis, evaluation, and inference, as well as explanation of the evidential, conceptual, methodological, criteriological, or contextual considerations upon which that judgment is based” ( Facione 1990 ; Table 1).

3. “Critical thinking is skilled, active interpretation and evaluation of observations, communications, information, and argumentation as a guide to thought and action” ( Fisher and Scriven 1997 , p. 20).

These three examples illustrate what Ennis (2016 , p. 11) considers to be the defining processes of critical thinking, namely, “the abilities to analyze, criticize, and advocate ideas” and “reach well-supported … conclusions.” These proactive processes represent the conceptual core.

Aside from the conceptual core, Ennis (2016) suggests that variations or distinct conceptions of critical thinking can be proposed without endangering the core concept. These variations arise from particular teaching and assessment situations to which the core concept is applied and operationalized. For example, in reviewing four different examples of particular teaching and assessment cases [i.e., Ennis’s (1996) Alpha Conception, Clemson’s (2016) Brief Conception, California State University (2011) , and Edward Glaser’s (1941) Brief Conception of Critical Thinking], Ennis (2016) explains that in each case the concept of critical thinking is operationalized to have a particular meaning in a given context. Ennis (2016) concludes:

In sum, differences in the mainstream concept [of critical thinking] do not really exist, and differences in conceptions that are based on the mainstream concept of critical thinking are usually to a great extent attributable to and appropriate for the differences in the situations of the people promoting the conception. (p. 13)

Building on Ennis’ (2016) proposal, then, a conception of critical thinking is offered herein to serve a specific purpose: to teach and assess critical thinking skills in data-rich environments. To do this, the core concept of critical thinking must include those information processes that guard against manipulability in data-rich environments.

Reactive Processes in Critical Thinking

Educational interventions and assessments must address reactive processes if they are to bolster critical thinking in non-idealized conditions. This is especially important in data-rich environments where information is likely to be novel, abundant (almost limitless), and quickly accessible. The tendency for people to simplify their information processing is amplified in data-rich environments compared to data-poor environments where information is routine and can be comfortably processed serially (e.g., writing a term paper on a familiar topic with ample time allowance). The simplification of data is necessary as the human brain only processes about 5–7 pieces of information in working memory at any one time ( Miller, 1956 ; see also; Cowan, 2001 ). This limitation exists atop the more basic limitation of what can be consciously perceived in the visual field ( Kroll et al., 2010 ). Thus, human beings instinctively simplify the signals they receive in order to create a manageable information processing experience ( Kroll et al., 2010 ).

Most of the information simplified and perceived will be forgotten unless it is actively processed via rehearsal and transfer into long-term memory. However, rehearsed information is not stored without error. Storage contains errors because another limitation of information processing is that memory is a constructive process ( Schacter, 2012 ). What is encoded is imbued with the schemata already in memory, and what is then retrieved depends on how the information was encoded. Thus, aside from the error-prone simplification process that permits the human information process to perceive successful navigation of the environment, there is the error-prone storage-and-retrieval process that characterizes memory. Data-rich environments accentuate these significant limitations of human information processing. Consequently, identifying both proactive and reactive information processes is necessary to generate realistic educational interventions and assessments that can help 1) ameliorate thinking bugs in today’s data-rich environments while at the same time 2) cultivating better mindware for critical thinking.

The Tripartite Model of Critical Thinking

One of the largest problems with modern initiatives to teach and assess critical thinking in data-rich environments is the neglect of empirically based theoretical frameworks to guide efforts ( Leighton, 2011 ). Without such frameworks, the information processes taught and measured are primarily informed by philosophical instead of psychological considerations. The former emphasizes proactive over reactive processes but both are needed. The emphases on proactive processes does not actually help educators identify and rectify the existing bugs in students’ mindware.

The conception of critical thinking that is advanced here is based on Stanovich and Stanovich’s (2010 ; see also Stanovich, 2021 ) Tripartite Model . The model focuses on both proactive and reactive processes. Unlike philosophical treatments of critical thinking, the tripartite model devotes significant attention to biased and error-prone information processing. According to Stanovich and Stanovich (2010 , p. 219; italics added): “the tendency to process information incompletely has been a major theme throughout the past 30 years of research in psychology and cognitive science ( Dawes, 1976 ; Taylor, 1981 ; Tversky and Kahneman, 1974 ).” The tripartite model does not provide a simple definition of what critical thinking entails given the complexity of the processes involved. Instead, it provides an outline of three levels of mindware that have been found to be constantly interacting in the process of critical thinking.

Three Levels of the Mind

The tripartite model integrates decades of cognitive and neuroscientific research, ranging from Tversky and Kahneman’s (1974) early work on biases and heuristics to the later work on dual process models of thinking ( Evans, 2003 ). The model shown in Figure 2 illustrates the relations between three distinct levels of information processing–the reflective mind (RM), the algorithmic mind (AM), and the autonomous mind (AUM). In Figure 2 , the level of information processing that functions to manipulate data in working memory, store, retrieve, and generate responses is the AM. This is the level that is directly on display and observed when human beings process and respond to questions, for example, on educational assessments and tests of intelligence. The AM can be defined by its processing speed, pattern recognition and retrieval from long-term memory, and manipulation of data in working memory.

FIGURE 2 . Adapted tripartite model ( Stanovich and Stanovich, 2010 ) to illustrate the connections among three different aspects or minds integral to human cognition.

The AM takes direction from two sources–the reflective mind or RM and the autonomous mind or AUM. The AUM is the subconscious part of human information processing that retains data acquired by means of imprinting, tacit and procedural learning, and emotionally laden events, resulting in many forms of automatic responses and implicit biases. The AUM is the level at which encapsulated or modularized knowledge can be retrieved to generate a quick and simplified response, which exerts minimal load on working memory. Depending on the influence of the AUM, the AM is capable of biased or unbiased responses. For example, in view of what appears to be a large insect, the AUM signals the AM to focus on getting out of the way. This is a biased response but it is an expedient response that is often observed in logical tasks (see Leighton and Dawson, 2001 ).

Unlike the AUM, the RM is a conscious and deliberative aspect of human information processing. The RM is the part of information processing that involves goals, beliefs, and values. It is the part of the mind that provides intentionality to human behavior ( Dennett, 1987 ). It directs the AM to suspend simple processing and expend the cognitive effort to deeply process information. The RM also functions to direct the AM to resist or override signals from the AUM to respond too quickly. Thus, it is the information processing directed by the RM–to engage and suspend certain processes - that needs to be the focus of most educational interventions and assessments of critical thinking.

Decoupling and Simulation Processes

According to Stanovich and Stanovich (2010) , the RM directs the AM to engage in two forms of proactive information processes. Both require cognitive effort. First, decoupling involves the process of suspending prior beliefs and attending to information in the context in which it is provided. For example, decoupling processes have been examined in belief bias studies ( Leighton and Sternberg, 2004 ). In these studies, participants are typically asked to evaluate arguments that have been created to differ along two dimensions–logical soundness and believability of conclusion. For example, a logically flawed argument is paired with a believable conclusion, for example, All politicians are liars; All crooks are liars; Therefore, all politicians are crooks. In response to these types of arguments, participants have been found to accept conclusions that are believable rather than logically sound. However, performance can be improved by instructing participants to explicitly consider the structure of the argument. In other words, the instructions are clearly designed to engage the RM. When explicit instructions are included, participants will show improved performance in correctly rejecting conclusions from flawed arguments.

Second, for decoupling to work, simulation is often activated in tandem. Simulation involves the process of actively considering distinct ways of interpreting information. For example, shown in Figure 3 are two panels showing distinct interpretations of the premises of the argument provided earlier about politicians and crooks. The one on the left shows the easiest interpretation or mental model of the argument about politicians (conclusion - All politicians are crooks ). The interpretation shown on the left is one which often may correspond to prior beliefs. On the right, an additional interpretation can be created to indicate that no politicians are crooks . The interpretation shown on the right may be less common but equally plausible given the premises of the argument. The effort to create additional interpretations or simulate information that contradicts prior beliefs has been found to correlate positively with working memory capacity ( Johnson-Laird and Bara, 1984 ). In fact, both decoupling and simulation have been found to require significant working memory resources and, thus, cognitive effort for participants to willingly adopt ( Johnson-Laird and Bara, 1984 ; Leighton and Sternberg, 2004 ; Stanovich, 2011 ; Leighton and Sternberg, 2012 ).

FIGURE 3 . Two types of information interpretations.

Most classroom assessments and achievement tests, even those that are purportedly designed to be cognitively complex, are not developed to evaluate whether students can decouple or simulate thinking ( Leighton, 2011 ). Instead most tests are developed to measure whether students can reproduce what they have learned in the classroom, namely, a form of optimal performance given instruction ( Leighton and Gierl, 2007 ; Stanovich and Stanovich, 2010 ). Often, then, there is little incentive for students to begin to suspend beliefs and imagine situations where what they have been told does not hold. Not surprisingly, most students try to avoid “overthinking” their responses on multiple-choice or even short-answer tests precisely because such simulated thinking could lead to choosing an unexpected or non-keyed response.

Suspending Serial Associative Processing

Unlike the thinking evoked by most classroom and achievement tests, information processing in data-rich environments calls for a different standard of evaluation. Data-rich environments typically offer students the possibility to navigate freely through multiple sites, unrestricted by time limits and/or instructions about how their performance will be evaluated. In such open, data-rich environments, individuals set their own standard of performance. According to the tripartite model, serial associative processing is likely to be the standard most often set by individuals. Serial associative processing is directed by the RM but it is simple processing nonetheless. It means that information is accepted as it is presented or rejected if it fails to conform with what is already known (prior beliefs). There is no decoupling or simulation. Johnson-Laird and Bara (1984 ; Johnson-Laird, 2004 ) called this simple type of processing single-model reasoning because information is attended and processed but goes unchallenged. Serial associative processing is different from the automatic responses originating in the AUM. Serial associative processing does involve analysis and evaluation but it does not consider multiple perspectives and so it is biased in its implementation.

Critical Thinking as Coordinated Suspension and Engagement of Information Processes

Consider again the defining processes Ennis (2016 , p. 11) proposes for critical thinking: “the abilities to analyze, criticize, and advocate ideas” and “reach well-supported … conclusions.” In light of Stanovich and Stanovich’s (2010) model, the processes mentioned by Ennis only reflect the AM and do not reflect the coordinated effort of the RM and AM to suspend serial associative processing and engage in decoupling and simulation. In other words, what is missing in most traditional conceptions of critical thinking are reactive processes, namely, processes that lead thinking astray such as serial associative processing, which must be suspended for better thinking to emerge.

In data-rich environments, actively resisting serial associative processing is a necessary component of critical thinking. This form of information processing must be actively resisted because the incentive is for individuals to do the opposite in the wake of massive amounts of information. Although applying this resistance will be cognitively effortful, it can be learned by teaching students to become more meta-cognitively aware of their information processing. However, even meta-cognitive awareness training is unlikely to help students resist serial associative processing, if critical thinking is under-valued by the RM. Thus, the design of teaching interventions and assessments must consider the construct of critical thinking not as a universally accepted and desired form of thinking but as a skill that students choose to apply or ignore ( Leighton et al., 2013 ). Consequently, interventions must persuade students of the benefits associated with critical thinking and assessments need to measure the processes that are most relevant for critical thought (e.g., decoupling and simulation). In the next section, Shavelson et al.’s (2019) assessment framework is used to illustrate how specific educational assessment designs can build on the tripartite model of critical thinking, and provide a more defensible conceptual foundation for inferences about critical thinking in data-rich environments.

Measuring Decoupling and Simulation: Shavelson et al.’s (2019) Assessment Framework

Shavelson et al.’s (2019) assessment framework is premised on three objectives. First, performance assessments are appropriate for measuring higher-level thinking constructs; second, assessments of higher-level thinking constructs should be developed in ways that clearly link scores to claims about postsecondary students’ capabilities; and third, higher-level thinking constructs, such as critical thinking, should require postsecondary students to make sense of complex information outside typical classroom environments. Each of these objectives is elaborated and connected to measuring key information processes for critical thinking.

Performance Assessments

Performance assessments typically contain tasks (i.e., selected and constructed) that require test-takers to attend to multiple types of materials (e.g., articles, testimonials, videos) and generate responses that involve an evaluation of those materials for the purpose of providing a reasoned answer on a topic. The topic is often novel and the tasks are complex such as evaluating a claim about whether a privately funded health-care clinic should be adopted by a community. The goal of a performance assessment is to approximate the informational demands of a real-world situation, calling on individuals to have to weigh different perspectives in the process of analyzing and evaluating materials.

The motivation to approximate real-world situations is a requirement in performance assessments. The constructs measured need to be assessed in the types of situations that justify making claims about what the test-taker can do in a context that approximates real-life. For example, performance assessments would not be the tool to use if the objective was to measure criterion or optimal performance ( Stanovich and Stanovich, 2010 ), that is, whether someone has learned the normative timeline for the Second World War or to factor polynomials. Both of these objectives do not reflect the types of skills required in complex environments, where typical performance is sought in determining whether the test-taker can invoke and manage specific information processes in providing a response.

Measuring the Mindware

In Shavelson et al.’s (2019 , p. 4) framework, the environments or contexts in which to measure critical thinking are broadly conceived:

a. contexts in which thought processes are needed for solving problems and making decisions in everyday life, and

b. contexts in which mental processes can be applied that must be developed by formal instruction, including processes such as comparing, evaluating, and justifying.

In considering both these measurement contexts, data-rich environments satisfy both. For example, the real-life contexts in which people must solve problems and make decisions nowadays typically involve seeking, analyzing, and evaluating a lot of information. Most of this information may be online where there is almost no oversight on quantity or quality control.

However, people do not solve problems and make decisions in a cognitive vacuum. This is where Stanovich and Stanovich’s (2010) tripartite model provides the necessary conceptual foundation to Shavelson et al.’s (2019) assessment framework for measuring critical thinking. The beliefs and values of the RM direct the type of information that is sought and how that information should be analyzed. Heretofore, the idea of values has not been elaborated. The valuing of critical thinking or stated differently, holding the value that beliefs should line up with evidence provides an impetus for engaging in effortful thinking. Churchland (2011) indicates that such values–what we consider good, bad, worthwhile or not–are rooted in the brain and have evolved as mechanisms to help human beings adapt and survive. Thus, the question for the reflective mind is one of why is critical thinking beneficial for me? Consequently, the design of performance assessments must include opportunities for measuring two fundamental catalytic processes for critical thinking: (a) whether the RM values critical thinking and for what reasons and (b) how the RM then directs the AM to engage or suspend serial associative processing for analyzing and evaluating the resources provided so that critical thinking can be achieved. The reason for measuring whether the RM values critical thinking is to establishing that a student is indeed motivated to engage in the effort it requires. A student may value critical thinking but not know how to do it, but it is also necessary to determine whether a student does not value it and therefore, irrespective of having the skills to do it, chooses not to do it. The educational intervention for each of these scenarios will be different depending on the cognitive and affective state of the student ( Leighton et al., 2013 ).

The question of how this engagement or suspension is measured is not trivial as it would involve finding a way to measure test-takers’ epistemic values, prior beliefs, and biases about the topic. Moreover, it would involve providing confirming or disconfirming sources of data in the assessment at different levels of quality. As test-takers select data sources to analyze and evaluate, evidence of the active suspension of prior belief (i.e., decoupling) and rejection of information at face value (i.e., simulation) needs to be collected to warrant the claim that the information processes inherent to critical thinking were applied.

Creating the Performance Assessment

According to Evidence Centered Design (ECD; Mislevy et al., 2003 ), an assessment is most defensibly designed by paying careful attention to the claim that is expected to be made from the assessment performance. In the case of Shavelson et al.’s (2019) assessment framework, the following high-level claim is desired:

[T]he assessment task presented here taps critical thinking on everyday complex issues, events, problems, and the like. The evidence comes from evaluating test-takers’ responses to the assessment tasks and potential accompanying analyses of response processes such as think-aloud interviews or log file analyses. (p. 9)

Because the claim includes ‘ critical thinking on everyday complex issues, events, problems, and the like ’ it becomes necessary to situate this claim within the specific data-rich environment that is of most interest to the developer but also the environment that is of most interest to the test-taker. In data-rich environments, thinking will not be general but specifically guided by the relevance of topics. In particular, what is essential to consider in such environments is that individuals are unconstrained by how they search and attend to information given the vast quantity and quality of sources. Thus, test takers’ value proposition of thinking critically for a given topic needs to be considered in their performance. If respondents do not value it, they are unlikely to engage in the effort required to suspend serial processing. And claims about what they can or cannot do will be less defensible.

At the outset of a performance assessment, a test-taker who does not value critical thinking for a given topic is unlikely to engage the critical information processes expected on the assessment. The following four facets of the data that Shavelson et al. (2019) indicate must be attended are unlikely to be invoked in depth:

1. Trustworthiness of the information or data—is it reliable, unreliable, or uncertain?

2. Relevance of the information or data—is it relevant or irrelevant to the problem under consideration?

3. Manipulability of the information to judgmental/decision/bias—is the information subject to judgmental errors and well-known biases?

4. Solution to the story problem—is the problem one where a judgment can be reached, a decision recommended, or a course of action suggested?

Each of these facets forms the basis of a question that is designed to direct the algorithmic mind (AM) to process the data in a particular way. However, the AM is an information processor that does not direct itself; it is directed by the RM. Consequently, for each of these facets, it is important to consider that both the RM and the AM are being induced and measured. For example, if critical thinking is to be demonstrated, all facets–trustworthiness, relevance, manipulability, and solution generation–require the RM to direct the AM to (a) override the autonomous mind (AU) in its reactionary response, (b) suspend serial associative processing, (c) decouple from pre-existing beliefs, and (d) simulate alternative worlds where the information is considered in the context in which it is presented. Although it is beyond the scope of the paper to illustrate the interplay of the RM and AM for each of these four facets, an example may suffice. Consider a critical thinking task that begins with a story about the delivery of a new vaccine for inoculating people against the COVID19 virus. After presentation of the story, the first item needs to probes the RM - whether the test-taker indicates importance in comprehending a story about vaccine safety. If the test-taker responds “yes,” the self-report can be validated against eye tracking reaction time data to check its validity (assuming greater importance would lead to more time spent reading). The second set of items can then probe the test-taker’s analysis of the trustworthiness of the information, for example, is the story reliable and how do you know? What information was irrelevant (e.g., the color of the viles) and was it decoupled from relevant information (e.g., the temperature at which the vaccine must be stored)? What variables in the story were re-imagined or simulated (e.g., transportation of a vaccine across multiple freezers might erode its integrity), leading to a different conclusion than the one stated in the story. The response to these second set of items must be evaluated, in aggregate, against the response for the first item in order to determine the rigor of AM thinking devoted to analyzing the veracity of the story and it elements. If the second response is weak, in light of a motivated RM, then one might generate the inference that the test-takers lacks the essential skills to think critically.

The induction of the RM to engage the AM in a specific manner in a performance assessment becomes an integral part of the critical thinking construct that is being measured in data-rich environments. In fact, one of the most important questions to be presented to test-takers before they engage with a performance measure of critical thinking might be a question that directly probes the RM to reveal the goals that drive its performance–does the RM value holding beliefs that are in line with evidence? In the absence of inducing the RM to accept the objective of the performance assessment, the RM’s direction of the AM will simply reflect the least effortful course of thinking.

Shown in Figure 4 are examples of preliminary questions to ask the respondent at the initiation of the performance assessment. These would be required to measure the meta-cognitive approach adopted by the test-taker in the specific data-rich environment in which the performance assessment is embedded. By incorporating preliminary questions into the design of the assessment such as how do you define critical thinking and do you value it , the assessment yields two sources of evidentiary data about the test-taker: First, what do they believe critically thinking entails? And second, are they motivated to demonstrate this type of thinking, namely, the construct of interest? Both these sources of data about the test-taker would help in the interpretation of their assessments results. If test-takers can define critical thinking but do not value it or are not willing to suspend associative serial processing, low scores may only reveal their lack of interest or motivation. The latter of which becomes a key challenge for educational interventions unless the reasons for its benefits can be shown.

FIGURE 4 . Refining the connections among three different aspects or minds ( Stanovich and Stanovich, 2010 ) integral to engaging facets of critical thinking on performance assessments ( Shavelson et al., 2019 ) in data-rich environments.

Moving Beyond Just Teaching Critical Thinking Skills

How well educators are poised to teach and assess critical thinking in data-rich environments might depend less on a specific instructional formula and more on how it is incentivized for students. In other words, there needs to be a clear message to students about what it is that they gain by suspending personal biases and engaging analytical strategies; for example, “Did you know that by becoming aware that you are reacting positively to the flashiest site of health information you may not be getting the best information? Or “Did you know that in searching for information about a political issue you will typically be drawn to information that confirms your prior beliefs? If you want to be fully prepared for debates, try searching for information that challenges what you believe so you can be prepared for both sides of the argument.”

A shortcoming with almost all assessments of critical thinking as of the writing of this paper is that they are designed from traditional definitions of critical thinking; meaning that these assessments do not test for cognitive biases explicitly. For example, the Halpern Critical Thinking Assessments ( Butler, 2012 ) measure five dimension of critical thinking premised on traditional conceptions of critical thinking (i.e., verbal reasoning, argument analysis, thinking as hypothesis testing, likelihood/uncertainty, and decision making and problem solving) but not cognitive biases. Another popular critical thinking test is the Cornell Critical Thinking Level Test Z ( Ennis and Millman, 2005 ) which measures induction, deduction, credibility, identification of assumptions, semantics, definitions, and prediction in planning experiments. However, all these attributes are proactive and not reactive. Only measuring proactive attributes can almost be viewed, ironically, as yet another instance of our tendency to confirm biases. What is needed is actively falsifying what we believe–testing the limits of what we want to think is true. There are at least two notable exceptions to the typical critical thinking tests. One is the Cognitive Reflection Test ( Frederick, 2005 ), which measures a person’s skill at reflecting on a question and resisting answering with the first response that comes to mind. In essence, this test measures reactive processes. The other is the Comprehensive Assessment of Rational Thinking (CART) by Stanovich (2016) . The CART is focused on measuring the preponderance and avoidance of thinking errors or contaminated mindware. For example, the CART contains 20 subtests that assess tendencies toward overconfidence, showing inconsistent preferences and being swayed by irrelevant information. Critical thinking tests designed to measure avoidance of reactive processes are relatively new and perhaps not surprisingly there are no large-scale studies of whether it can be effectively taught. It is for this reason that the work we present here is necessary and we believe presents a contribution to the literature.

Proactive critical thinking can be taught so there is no reason to think that awareness of reactive critical thinking cannot also be taught. To be sure, most of the research on teaching critical thinking skills has been in the area of proactive skills. A meta-analysis of strategic approaches to teaching critical thinking uncovered that various forms of critical thinking can be taught with measurable positive effects ( Abrami et al., 2015 ). However, the average effect size of educational interventions was 0.30 (Cohen’s d); thus, weak to moderate at best ( Cohen, 1977 ; Abrami et al., 2015 ). Part of the challenge is that critical thinking, like any other disposition and/or skill, takes time to cultivate and uptake is determined by how well the audience (students) buys into what is being taught.

One would expect different approaches for teaching critical thinking depend not only on the specific goal of instruction but also how well students believe in the benefits articulated. For example, Lorencová et al. (2019) conducted a systematic review of 39 studies of critical thinking instruction in teacher education programs. The most often cited targeted skills for instruction were analysis and evaluation. A majority of the educational interventions had the following characteristics: (a) took place during a course in one semester with an average number of 66 students, (b) were face-to-face, (c) used infusion (i.e., critical thinking added as a separate module to existing curriculum), or immersion (i.e., critical thinking integrated into the full curriculum) as the primary context for instruction with (d) discussion and self-learning as tools for pedagogy. The most frequently used standardized assessment tool for measuring learning gains was the CCTDI or California Critical Thinking Disposition Inventory, which is a measure of thinking dispositions instead of actual critical thinking performance.

In addition to the CCTDI, most instructors also developed their own assessments, including assessment of typical case studies, essays, and portfolios. Most of the 39 studies reviewed showed fully positive or some positive results; only 3 studies reported null results. Not surprisingly, however, larger effects between pre- and post-intervention were observed for studies employing instructor-created , non-standardized tools compared to standardized assessment tools.

One of the biggest challenges identified by Lorencová et al. (2019) is not with the interventions of critical thinking but with assessments to measure gains. Instructor-developed assessments suffer from a variety of problems such as demand characteristics, low reliability, and potentially biased grading. Thus, little can be concluded about what reliably works among the many strategies for critical thinking without good measures. A related problem is that many of these interventions do not indicate how long the effects last; good measures are also required to gauge the temporal effects of interventions. Additional problems that often plague intervention studies involve relatively small sample sizes. These challenges may be overcome in a variety of ways. For example, moving away from idiosyncratic instructor-developed critical thinking assessments and moving toward the establishment of a consortia of researchers that can pool their items for review, field-testing, refinement and ultimately leverage large enough samples to establish reliable norms for inferences. Toward this end, Shavelson et al. (2019) exemplify this work in their International Performance Assessment of Learning (iPAL) consortium.

In another recent review of critical thinking interventions in professional programs in the social sciences and STEM fields, Puig et al. (2019) noted the prevalence of unstandardized forms of assessments for measuring critical thinking, most of which were qualitative. For example, Puig et al. (2019 , p. 867) indicate that most of the studies they reviewed based their results largely on “the opinions of students and/or teachers, as well as on other factors such as students’ motivation, or their level of engagement to the task... students’ perceptions, learning reflections and their participation in the task, and others even did not assess CT.” These measures may begin to probe the values and beliefs of the RM but they ignore the information processes of the AM in instantiating critical thinking.

Schmaltz et al. (2017) indicate that part of the reason educators at all levels of instruction, including postsecondary institutions, find it so challenging to teach critical thinking is that it is not well defined and there are not enough empirical studies to show what works. Although the deficits raised by Schmaltz et al. (2017) are justified, the problem of showing what works requires measuring human behavior with minimal bias. Thus, the deficits identified by Schmaltz et al. (2017) may actually reside more with the assessments used to evaluate interventions than with the interventions themselves. Just as there many ways to teach algebra or essay composition successfully depending on the students involved, so must teaching critical thinking take on different methods as shown in the literature (e.g., Abrami et al., 2015 ; Lorencová et al., 2019 ). However, focusing so intently on the specific characteristics of educational interventions may hurt more than it helps if it distracts from the assessments that need to be designed to measure changes in thinking. In whatever form critical thinking is taught, what is certainly needed are assessments that reliably measure the construct of critical thinking, however it has been conceptualized and operationalized ( Ennis, 2016 ).

Teaching and Assessing Critical Thinking in Data-Rich Environments

Teaching and assessing critical thinking in data-rich environments requires not only a conception of what critical thinking entails in such environments but also an adequate assessment of the information processes associated with this type of thinking. Building first on Stanovich and Stanovich’s (2010) tripartite model, the instructional goals must include (a) students becoming self-aware of what types of thinking they value and in what circumstances and (b) students learning to apply strategies they believe are valuable in thinking critically in identified circumstances. The premise is this: If critical thinking is valued for a given topic, strategies such as decoupling and simulation can be explicitly taught, taken up by students, practiced and assessed using online information sources and tasks. This is also where Shavelson et al.’s (2019) framework provides an excellent assessment foundation to structure teaching and assessment modules. Prompts and performance tasks can be embedded throughout digital modules to assess students’ goals for information processing, strategies for searching and analyzing data tables, reports, and graphs for the stated goals, time spent on different informational resources, and evaluation of conclusions.

Teaching and assessment modules for critical thinking must motivate students to expend the cognitive resources to suspend certain information processes (e.g., serial associative processing). As previous reviews have found (e.g., Lorencová et al., 2019 ), motivation is a pre-requisite to decouple and simulate as these are cognitively taxing forms of processing. In the pre-development stage of any teaching or assessment form, one of the most important tasks is to survey the population of students about interests warranting critical thinking. Then, digital teaching modules and assessments can be designed around topics that would motivate students to expend the resources needed to engage with tasks; for example, the effects of social media on mental health, the cost and value of postsecondary education or even a learning disability can be used as topics to spark the interest of students. Starting from a position of awareness about the topics that warrant attention, students can be invited to learn about resisting serial associative processing in the collection of data (e.g., finding high-quality data that are relevant but opposed to what is believed about a topic), decoupling in the analysis of conclusions (e.g., looking at statistics that do not misrepresent the data), and simulation in evaluations of conclusions (e.g., weighing the evidence in line with its quality).

However, incentivizing students to pay attention to what they are processing does not mean it will be processed critically. Especially when topics are of interest, individuals are likely to hold strong opinions and seek to actively confirm what they already believe. Thus, teaching modules must begin with a process of having students become aware of a bias to confirm, and invoking reminders to students that this bias can surface unless it is constantly under check in their self-awareness. For example, a prompt for students to become aware of their biases can be integrated into the introductory sections of a teaching and assessment module. Prompts can also be designed to remind them of the critical thinking they have indicated they value. Previously presented information processes (e.g., suspending prior beliefs or decoupling) can be flashed as reminders in searching, assessing task information, and evaluating conclusions. Another option might be to have students choose to assume the perspective of a professional such as a journalist, a lawyer, or a counselor and to challenge them to process information as that professional would be expected to do.

Consider the following screenshot in Figure 5 from a storyboard associated with the design of a teaching and assessment module on autism . Following an introductory screen, participating students are advised in the second screen that they are going to be learning about ways to collect, manage, evaluate, and apply data on autism. The third screen introduces them to the research project and poses initial questions they are unlikely to be able to answer critically. The fourth screen introduces them to potential data sources, such as an online report from a mainstream news channel and a report from a national statistics agency. At this point, students can be prompted to rate the trustworthiness of the sources, which reflects the first facet of Shavelson et al.‘s framework. In the fifth screen, students navigate to the data source(s) selected. Irrespective of the data source selected, students are probed on the manipulability and relevance of the data source, and how it advances the investigation. At each point during the module, students are scaffolded in evidence-based learning about autism and asked to provide responses designed to reveal their chosen information processing. For example, in the fourth screen where students are asked to list the data sources for autism, the sources students indicate can be categorized according to at least two dimensions. First, is each source trustworthy? Relevant? Second, how much time and effort did students spend analyzing the sources (using reaction time data). If students appear to carefully choose what they think are trustworthy and relevant sources but do not ascribe the trustworthiness or relevance to substantive criteria, then students may value critical thinking (RM) but do not have the knowledge or skills to properly direct this value (RM) in their information processing. In this case, the scaffolding comes in the form of an instructional part of the module that explains the criteria that should be used for judging reliability, and relevancy in the case of neurodevelopmental disorders such as Autism.

FIGURE 5 . Example of story board to illustrate the design of a digital teaching and assessment module of critical thinking in the area of autism.

The storyboard shown in Figure 5 does not show how students’ potential bias may be assessed at the beginning of the module. However, opportunities to bring bias into students’ awareness can be inserted as is shown in the fourth screen in Figure 6 . Following this fourth screen, another screen (not shown) could be inserted to teach students what it means to decouple and simulate in the process of information processing. For example, the instructional module can show why an uncontrolled variable (e.g., a diet supplement) should be decoupled from another variable that was controlled (e.g., age of the mother). In this way, students are reminded of their biases (e.g., diet supplements are bad for you), instructed on what it means to think critically in an information-rich environment and also prompted to decide whether such strategies should be applied in considering data during the assessment.

FIGURE 6 . Example of how to insert a probe for students to consider their own biases about the topic of autism.

Discussion and Conclusion

New ways of teaching and assessing critical thinking in data-rich environments are needed, given the explosion of online information. This means employing definitions of critical thinking that explicitly outline the contaminated mindware that should be avoided in data-rich environments. The democratization of information in the digital age means that anyone, regardless of qualifications or motivation, can share stories, ideas, and facts with anyone who is willing to read, watch, and be convinced. Although misinformation has always existed, never before has it been as ubiquitous as it is today and cloaked in the pretense of trustworthiness as found on the world-wide web. Errors in reasoning and bias in information processing are, therefore, central to the study of critical thinking ( Leighton and Sternberg, 2004 ). Consequently, three lines of thinking were presented for why a refined conception of critical thinking in data-rich environments is warranted. First, traditional definitions of critical thinking typically lack connections to the information processes that are required to overcome bias. Second, data-rich environments pose cognitive traps in critical thinking that require more attention to bias. Third, personal dispositions such as motivation are more important than previously thought in the teaching and measurement of critical information processes. Because the present paper is not empirical but rather conceptual, we end not with main findings but with essential take home ideas. The first essential idea is that explicitly articulating a refined conception of critical thinking, one that includes reactive processes and/or mindware, must become part of how good thinking is described and taught. The second essential idea is that teaching and assessing proactive and reactive processes of critical thinking must be empirically examined.

The contemporary teaching and assessment of critical thinking must be situated within environments that are rich in data and evoke more than proactive but mechanistic information processes of analysis and evaluation. Teaching and assessment of critical thinking in data-rich environments must become more sophisticated to consider students’ 1) interest in the topics that merit critical thinking, 2) self-awareness of human bias, and 3) how both interest and self-awareness are used by students’ reflective minds (RM) to guide strategic application of critical-thinking processes in the AM. A conceptual refinement of critical thinking in data-rich environments, then, must be based on a strong theoretical foundation that presents a coordination of the reflective, algorithmic, and autonomous minds ( Stanovich and Stanovich, 2010 ). This is provided by Stanovich and Stanovich’s (2010) tripartite model and supported by decades of empirical research into human thinking processes ( Leighton and Sternberg, 2004 ; Kahneman, 2011 ; Stanovich, 2012 ; Shavelson et al., 2019 ).

A theoretical foundation for operationalizing a new conception of critical thinking, however, is useless for practice unless there is a framework that permits the principled design of teaching and assessment modules. Shavelson et al. (2019) provides such a framework. Shavelson et al. (2019) assessment framework provide the structure for generating performance-based tasks that evoke the reflective and algorithmic information processes required of critical thinking in data-rich environments.

The mindware that students download in performance assessments of critical thinking must reflect the sophistication of this form of information processing. Most students do not acquire these skills in secondary school or even post-secondary education ( Stanovich, 2012 ; Ridsdale et al., 2015 ; Shavelson et al., 2019 ). Stanovich (2012 , p. 356) states: “Explicit teaching of this mindware is not uniform in the school curriculum at any level. That such principles are taught very inconsistently means that some intelligent people may fail to learn these important aspects of critical thinking.” He indicates that although cognitive biases are often learned implicitly, without conscious awareness, critical-thinking skills must be taught explicitly to help individuals come to know when and how to apply higher-level skills. Instruction in critical thinking thus requires domain-specific knowledge and transferable skills that allow individuals to 1) coordinate the RM and AM, 2) recognize bias, and 3) regulate the application of higher-level thinking strategies. A more sophisticated conception of critical thinking provides an opportunity to guide instructive and performance-based assessment programs in the digital age.

Author Contributions

The authors confirm being the sole contributors of this work and have approved it for publication.

Acknowledgements

Preparation of this paper was supported by a grant to the first author from the Social Sciences and Humanities Research Council of Canada (SSHRC Grant No. 435-2016-0114).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Stanovich, K. E., Toplak, M. E., and West, R. F. (2008). The development of rational thought: a taxonomy of heuristics and biases. Adv. Child. Dev. Behav . 36, 251–285. doi:10.1016/s0065-2407(08)00006-2

Taylor, S. E. (1981). The interface of cognitive and social psychology. In Cognition, social behavior, and the environment . Editor J. H. Harvey (Hillsdale, NJ: Erlbaum ), pp. 189–211.

Toplak, M. E., and Flora, D. B. (2020). Resistance to cognitive biases: longitudinal trajectories and associations with cognitive abilities and academic achievement across development. J. Behav. Decis. Making , 1–15. doi:10.1002/bdm.2214

Tversky, A., and Kahneman, D. (1974). Judgment under uncertainty: heuristics and biases. Science 185 (4157), 1124–1131. doi:10.1126/science.185.4157.1124

Wachowski, L., Wachowski, L., Silver, J., Reeves, K., Fishburne III, L., Moss, C. A., et al. (1999). The matrix . Burbank, CA: Warner Home Video .

Keywords: post-secondary education, critical thinking, data-rich environments, cognitive biases, performance assessments

Citation: Leighton JP, Cui Y and Cutumisu M (2021) Key Information Processes for Thinking Critically in Data-Rich Environments. Front. Educ. 6:561847. doi: 10.3389/feduc.2021.561847

Received: 13 May 2020; Accepted: 21 January 2021; Published: 24 February 2021.

Reviewed by:

Copyright © 2021 Leighton, Cui and Cutumisu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Jacqueline P. Leighton, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Thinking Clearly with Data

Ethan Bueno de Mesquita

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Thinking Clearly with Data: A Guide to Quantitative Reasoning and Analysis

• Ethan Bueno de Mesquita and Anthony Fowler

An engaging introduction to data science that emphasizes critical thinking over statistical techniques

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An introduction to data science or statistics shouldn’t involve proving complex theorems or memorizing obscure terms and formulas, but that is exactly what most introductory quantitative textbooks emphasize. In contrast, Thinking Clearly with Data focuses, first and foremost, on critical thinking and conceptual understanding in order to teach students how to be better consumers and analysts of the kinds of quantitative information and arguments that they will encounter throughout their lives. Among much else, the book teaches how to assess whether an observed relationship in data reflects a genuine relationship in the world and, if so, whether it is causal; how to make the most informative comparisons for answering questions; what questions to ask others who are making arguments using quantitative evidence; which statistics are particularly informative or misleading; how quantitative evidence should and shouldn’t influence decision-making; and how to make better decisions by using moral values as well as data. Filled with real-world examples, the book shows how its thinking tools apply to problems in a wide variety of subjects, including elections, civil conflict, crime, terrorism, financial crises, health care, sports, music, and space travel. Above all else, Thinking Clearly with Data demonstrates why, despite the many benefits of our data-driven age, data can never be a substitute for thinking.

• An ideal textbook for introductory quantitative methods courses in data science, statistics, political science, economics, psychology, sociology, public policy, and other fields
• Introduces the basic toolkit of data analysis—including sampling, hypothesis testing, Bayesian inference, regression, experiments, instrumental variables, differences in differences, and regression discontinuity
• Uses real-world examples and data from a wide variety of subjects
• Includes practice questions and data exercises

• Organization
• Who Is This Book For?
• Acknowledgments
• What You’ll Learn
• Introduction
• Abe’s hasty diagnosis
• Civil resistance
• Broken-windows policing
• Thinking and Data Are Complements, Not Substitutes
• Readings and References
• Fact or correlation?
• Description
• Forecasting
• Causal inference
• Mean, variance, and standard deviation
• Correlation coefficient
• Slope of the regression line
• Populations and samples
• Straight Talk about Linearity
• Wrapping Up
• What Is Causation?
• Potential Outcomes and Counterfactuals
• What Is Causation Good For?
• The Fundamental Problem of Causal Inference
• What is the cause?
• Causality and counterexamples
• Causality and the law
• Can causality run backward in time?
• Does causality require a physical connection?
• Causation need not imply correlation
• The 10,000-hour rule
• Corrupting the youth
• High school dropouts
• Suicide attacks
• Doctors mostly see sick people
• The Challenger disaster
• The financial crisis of 2008
• Life advice
• Regression Basics
• Linear Regression, Non-Linear Data
• Forecasting presidential elections
• How Regression Is Presented
• A Brief Intellectual History of Regression
• What Makes for a Good Estimator?
• Standard errors
• Small samples and extreme observations
• Confidence intervals
• Hypothesis testing
• Statistical significance
• Statistical Inference about Relationships
• What If We Have Data for the Whole Population?
• Social media and voting
• The Second Reform Act
• Can an octopus be a soccer expert?
• p -screening
• Get out the vote
• p -hacking forensics
• Reduce the significance threshold
• Adjust p -values for multiple testing
• Don’t obsess over statistical significance
• Requiring pre-registration in drug trials
• Football and elections
• The power pose
• Does the truth wear off?
• Francis Galton and Regression to Mediocrity
• Reversion to the Mean Is Not a Gravitational Force
• Does knee surgery work?
• The placebo effect
• Cosmic habituation explained
• Cosmic habituation and genetics
• Beliefs Don’t Revert to the Mean
• Charter schools
• Thinking Clearly about Potential Outcomes
• Confounders
• Reverse causality
• The 10,000-hour rule, revisited
• Campaign spending
• Contraception and HIV
• Mechanisms versus Confounders
• Thinking Clearly about Bias and Noise
• Party whipping in Congress
• A note on heterogeneous treatment effects
• The Anatomy of a Regression
• How Does Regression Control?
• Is social media bad for you?
• Reading a Regression Table
• Controlling for Confounders versus Mechanisms
• There Is No Magic
• Breastfeeding
• Randomization and Causal Inference
• Noncompliance and instrumental variables
• Chance imbalance
• Lack of statistical power
• Interference
• Military service and future earnings
• Are extremists or moderates more electable?
• Does continuity hold in election RD designs?
• Bombing in Vietnam
• Motivation and Success
• Parallel Trends
• Unemployment and the minimum wage
• Is watching TV bad for kids?
• Contraception and the gender-wage gap
• Do newspaper endorsements affect voting decisions?
• Is obesity contagious?
• The democratic peace
• Causal Mediation Analysis
• Cognitive behavioral therapy and at-risk youths in Liberia
• Do voters discriminate against women?
• Social pressure and voting
• Commodity price shocks and violent conflict
• Miles-per-gallon versus gallons-per-mile
• Percent versus percentage point
• Policy preferences and the Southern realignment
• Some rules of thumb for data visualization
• Bayes’ rule
• Information, beliefs, priors, and posteriors
• Abe’s celiac revisited
• Finding terrorists in an airport
• Bayes’ rule and quantitative analysis
• Screening frequently or accurately
• Metal detectors in airports
• Blood pressure and heart attacks
• Climate change and economic productivity
• Malnutrition in India and Bangladesh
• College admissions
• Why can’t major league pitchers hit?
• The duty on lights and windows
• The shift in baseball
• The war on drugs
• Cost-benefit analysis and environmental regulation
• Floss your teeth
• Wear a mask
• Algorithms and racial bias in health care
• How quantification shapes our values
• Think Clearly and Help Others Do So Too

"I very much recommend this book, not only to all that teach statistics to (under)graduate students, but also those that use statistics for their own research, that would like to value the work of others, or engage in debates using actual or perceived facts."—Gijs Dekkers, International Statsitical Review

“A common phrase one hears in public life is that correlations and causality are the same but different. But how are they the same and how exactly do they differ? Thinking Clearly with Data threads a needle between two advanced subjects by clearly laying out a theory of both. This book is destined to become a classic and, if we are lucky, will be on every social scientist’s shelf.”—Scott Cunningham, Baylor University

“Witty, erudite, and chock-full of memorable and engaging examples, Thinking Clearly with Data brings core statistical ideas to life. The insights it offers are helpful not only to scholars in search of creative research strategies but also to readers who are simply trying to make sensible everyday decisions on topics from parenting to personal finance.”—Donald P. Green, Columbia University

“By making thinking the primary focus in teaching data analysis, Thinking Clearly with Data fills a big need.”—Dustin Tingley, Harvard University

“Whether you are a social scientist engaged in research, an attorney pleading a case, or a patient deciding on a medical treatment, you need to read Thinking Clearly with Data . This timely—and useful—book for making decisions in the data-rich twenty-first century is one that everyone who thinks about evidence should read.”—Lynn Vavreck, University of California, Los Angeles

“ Thinking Clearly with Data gives readers the necessary tools to be critical consumers of claims that others make based on data, and even to start making credible claims based on data themselves.”—Andy Eggers, University of Chicago

“Rather than getting bogged down in the math and statistics underlying the methods, Thinking Clearly with Data walks students through the big ideas of what can be learned from data and flags common mistakes even well-trained data analysts make.”—Jonathan Davis, University of Oregon

“ Thinking Clearly with Data is one of the most accessible and welcoming books I’ve seen on how to make sense of the world with data, thoughtfulness, and rigor. It’s a must-read for anyone looking to be smarter in our data-driven world.”—Andrea Jones-Rooy, New York University

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9 Data Science Skills for Beginners

• 03 Aug 2021

The past two decades have seen a proliferation of data generation and collection. This trend has been driven by several developments, including the emergence of social media, e-commerce, smartphones, wearable technology, and the internet of things (IoT).

To the untrained eye, much of this data may appear as white noise, but in truth, it can be a valuable source of insight. Businesses that invest in data generation, collection, and analysis are often able to leverage it to inform decision-making and strategic initiatives. This has made data science skills extremely valuable for professionals looking to advance in their careers.

Below is a look at why data science is important to modern business, who should prioritize developing data science skills , and a list of skills that those new to data science should gain.

Access your free e-book today.

Who Needs Data Science Skills?

Data science skills are most important for professionals who directly work with data and need to strongly understand it to do their jobs (for example, data scientists, data engineers, and analysts).

Other professionals, however, can benefit from developing data science skills. Whether you’re an individual contributor, manager, or business leader, building your data science skills can empower you to:

• Find and evaluate data that may be relevant to your job, even if you don’t typically use data
• Become more data-driven in your decision-making
• Better communicate with others in your organization (especially those on the data team), as well as executives and members of the C-suite
• Tie your work back to its business case by understanding the key metrics executives care about, along with your contributions to those metrics
• Change your career to a more data-focused role

Regardless of how often you interact with data, a firm understanding of data science can be an asset to your career, especially as small- and mid-sized businesses embark on the data-driven path blazed by larger companies.

Data Science Skills for Beginners

1. basic data literacy.

To interact with data and those who work with it, you need to understand its key terms, concepts, and language.

This understanding is commonly known as data literacy . By developing your data literacy, you can effectively discuss different types of data, data sources, analysis, data hygiene, along with key tools, techniques, and frameworks. You can also leverage the steps in the data life cycle —which underlies most data projects—and elements of the data ecosystem .

Without basic data literacy, you’ll likely find it difficult to talk about or use data, making it one of the most important data science skills to develop as a beginner.

2. Domain Fluency

To effectively leverage data, you must first have a solid understanding of your domain: the trends, developments, challenges, opportunities, and other factors that not only affect your industry and organization, but also the work you perform.

While domain expertise isn’t a data science skill in and of itself, it can be difficult to know which data points are relevant to your work and industry without it. This, in turn, can make it challenging to generate, collect, evaluate, and analyze data. Domain fluency enables you to cut through the noise and identify the metrics and data points that are most useful to you.

3. Data Generation and Collection

Before data is manipulated and analyzed so you can glean insights from it, it must first be generated and collected. As such, data generation and data collection are the earliest—and arguably most important—steps in the data life cycle.

Depending on your role, you may not be in a position to generate or collect data. Still, it’s important to understand the different ways it can be generated and collected, such as surveys or questionnaires. Once you know what’s possible, you can more easily communicate with those who are responsible for data generation and collection.

4. Data Manipulation

Data is rarely useful in its raw state. This is because nearly every dataset includes errors, gaps, or information that’s unrelated to the business question at hand. For data to be analyzed, it must first be manipulated and transformed into something that can be more readily used.

Data can be manipulated in several ways. Data wrangling includes cleaning a dataset by removing errors and filling gaps. Data encryption makes a dataset more secure. Data compression makes it easier to store and query a set of data. While you don’t need to know how to perform all of these activities, understanding what goes into each can give you the vocabulary to speak about them and understand how they impact your project.

5. Analytical Skills

Analytical skills are ultimately what allow you to dig into a dataset and come away with insights that inform business strategy and other decisions. With that in mind, data analysis typically involves searching for and identifying trends, patterns, or outliers in data. Developing an analytical mindset that’s capable and comfortable working with numbers can prove critical if you hope to leverage data in your role.

Data analysis can be conducted in a variety of ways. Statistical modeling, data mining, artificial intelligence, machine learning, and algorithms are all powerful tools you can leverage to quickly digest large amounts of information.

6. Data Ethics

Organizations that collect, store, and analyze data are responsible for protecting that data from misuse. This includes sensitive data, such as medical information, and seemingly less sensitive data, such as purchase histories. Organizations that fail to protect data from unauthorized access or use it inappropriately risk losing consumer trust and can also face monetary fines and legal penalties.

With this in mind, it’s a good idea to understand the laws, rules, and regulations that dictate how data is used within your industry and organization. Familiarizing yourself with the concepts and core tenets of data ethics , data privacy , and data governance is also recommended.

7. Critical Thinking Abilities

Critical thinking is perhaps the most essential skill in this list. It’s by leveraging critical thinking skills that you can:

• Identify the metrics that matter most to your project and should be collected
• Evaluate a dataset for completeness and accuracy
• Identify trends and patterns, and seek to understand the “why” behind them
• Recognize potential biases or errors in a dataset or analysis
• Extrapolate insights you can use to inform business strategy

Without robust critical thinking skills, it’s more difficult to perform all of these activities. There’s also an increased risk that you may accept bad data or faulty analysis.

8. Communication Skills

Though data is a powerful tool, if you’re unable to communicate your analysis effectively, it can be difficult to turn insights into action. That’s why learning how to tell a story with data is crucial.

This often means adopting different communication practices, depending on your audience. For example, when you’re communicating with someone on the data team or another professional who’s familiar and comfortable with data, it’s important to use proper terminology so you don’t come across as uninformed.

On the other hand, if you’re communicating with someone who’s less data literate, you may need to simplify the message. Data visualization can be an especially powerful tool in such a case. By leveraging data visualization tools and techniques, you can generate easy-to-digest graphics and charts, which can be particularly useful when communicating with those who are uncomfortable with data.

9. Mathematical and Programming Skills

When it comes to working with large datasets and performing complex forms of analysis, having strong mathematical and programming skills can make the task much easier. These are among the more technical data science skills. Though they’re important for data scientists and analysts, they’re less of a priority for beginners learning data basics.

Some of the most important mathematical skills for data science include statistics, probability, algebra, and multivariate calculus. Some of the most valuable programming and coding skills for data science include programming languages R, Python, and SQL. In addition, it’s critical to understand how machine learning and artificial intelligence work.

Developing Your Data Science Skills

When learning data science and developing your skills, there are several routes you can take. Reading data science material and watching videos can be an excellent way of exposing yourself to the subject. While it’s possible to self-teach, many find that they learn more quickly and easily when material is presented in a structured way, such as through a course or workshop.

Are you interested in furthering your data literacy? Explore our online business essentials courses , and download our free data and analytics e-book to learn how you can use data for professional and organizational success.

About the Author

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Critical Thinking: Top Skill in Data Science

ARTICLE SUMMARY

Whether you’re brand new to data science , have gotten your feet wet in this field or are an expert, you should know that working with data is all about generating knowledge.

There are many handfuls of ways data can make things better. Consider these:

• Telling stories
• Exploring relationships
• Backing up decisions
• Finding patterns
• Judging experiments
• Answering questions

When anaylyzing data, the goal is to turn information into insights and in order to create insights about the right things, we must ask the right questions.

Practicing any of the above well would require one to have many “hard skills” like coding, visualization, data cleaning, math modelling, graph interpretations, etc.

What is far less talked about are the “soft skills” for making data useful, and of all the skills that a successful student or expert of data science needs to have,  critical thinking   is definitely at the top of that list . All data analysts and scientists should embrace critical thinking as a key skill and capability.

I’ve researched and investigated critical thinking for myself to build my own skills for data analysis:  What is it, why is it important, what are the benefits, when is it required and how can we get better at it?

Below, I answer these questions and include exercises and recommendations that can help improve on your own critical thinking skills.

What Is Critical Thinking?

Critical thinking is the sensible and unbiased analysis, examination and intepretation of data in order to form a usable and reasonable understanding.

A less verbose definition, critical thinking is  a   process of questioning information to make better decisions and understand things better.

Here are examples of some critical thinking skills:

• Objectivity
• Problem Solving

To better understand how to improve our critical thinking skills, we need to understand it fully first.

Why Is Critical Thinking Important?

To put this in perspective, critical thinking is the opposite of your day-to-day thinking. When we think, usually this happens naturally.

When we think critically, we are  consciously using our intellectual tools  to reach a well-founded conclusion than our brain naturally would.

Elements of thought that can act as the intellectual tools:

• Points of view
• Information
• Implications
• Assumptions

That information can come from sources like:

• Experiences
• Observations
• Reflections
• Communications

If we were to deliberately think about our every action, we wouldn’t have enough mental energy for the important things, so having much of our thoughts happen automatically can be a good thing.

It’s only when we  let our automatic mental processes rule important decisions, where it becomes an issue.  Without critical thinking, it’s easy for us to be manipulated and for all kinds of disasters to result.

What Are The Benefits of Critical Thinking?

Thinking critically or not can make the difference between success and failure in most areas of your life.

Here are 5 important ways critical thinking can impact your life:

1. Better Decisions

We make hundreds to thousands of decisions every single day. While most of them are naturally occuring and happening unconsciously, the most important decisions we make are the hard ones that require a lof of thought.

Critical thinking helps you cope with everyday problems as they come. It promotes independent thinking and makes you smarter and less likely to fall for lies, peer pressure, or scams.

2. Well-Informed

As technology advances more and more each year, we now have access to more information than we ever have before.

Critical thinking helps you sort through all the noise and can assure your opinions are based on the best available facts.

Happiness is more than just a feeling, it’s something we can all practice daily. Recognizing and understanding yourself is an underrated path to happiness.

Critical thinking is a great tool to be cognizant of yourself and learn to master your thoughts. You can free yourself from negative thoughts that might hold you back, help express your thoughts, ideas, and beliefs, and help you evaluate your strengths and shortcomings.

4. Successful Career

Critical thinking can help any profession that might require you to:

• analyze information
• solve problems
• find solutions
• be creative
• present ideas

Critical thinking is becoming a skill that is valued in many professions other than data science — it can gauge longstanding success.

5. Improve Relationships

When you utilize critical thinking in relationships, you are more open-minded and able to easily understand someone else’s POV.

You’ll also be able to spot when others are:

• Don’t have your best interests
• Taking advantage
• Manipulating

Critical thinking helps you become a voice of reason, less likely to jump to conclusions, more empathetic and easier to get along with.

What Are Some Situations Critical Thinking Is Used?

Here are come examples of critical thinking scenarios:

• A data scientist working with great precision through a complex experiment in an effort to gather and analyze data
• An EMT pulling up to a scene of an accident to analyze the situation, evaluate priorities, and theorize what actions to take
• A lawyer, judge, or jury systematically investigating, interrogating, examining, and evaluating evidence
• A writer organizing ideas for a plot of a story
• A parent anticipating the costs of sending kids to college, analyzing income and budgeting expenses
• A person trying to decipher a friend’s needs, expressed through a stampede of emotion and depracating comments, to give that friend help and support

In all of the above examples, proper critical thinking skills must be utilized to achieve the best solution.

How Can We Improve Critical Thinking?

The essence of the independent mind lies not in what it thinks, but in how it thinks” — Christopher Hitchens

The good news is that thinking critically is a learned skill, which can be cultivated in anyone. You simply are not born with innate critical thinking skills.

That said, if you want to be a good critical thinker, you need to remember that it’s all about practice. Like any habit, there are certain muscles you need to flex over time.

Below, you’ll find 5 ways to improve your everyday critical thinking. Focusing on these will put you on the path to becoming an exceptional critical thinker.

1. Identification

The first step in the critical thinking process is to identify the situation or problem. Once you have a clear picture of that, and the factors that may influence it, you can begin to dive deeper into it and its potential solutions.

How to Improve:  When faced with a new situation, question or scenario, stop to take a mental picture of how things are stacked up.

2. Ask Questions

“It is better to debate a question without settling it than to settle a question without debating it” — Joseph Joubert

Learn to question everything. It’s very easy to accept things at face value, but that’s a sign of a lazy mind.

How to Improve:  You can start by asking the following questions:

• What do you already know?
• How do you know that?
• What seems to be the reason for this happening?
• What are you trying to prove, disprove, demonstrate, critique, support?
• What are you overlooking?

You should continually go back to these basic questions you asked when you set out to solve the problem. These types of questions urge you to get right to the core of a problem, examining it for simple solutions before assuming its complexities.

3. Be Aware of Your Mental Process

Being a good critical thinker requires you to accept that you have biases. Although even the best critical thinkers will never be entirely bias-free, you need to learn to look out for them.

How to Improve: Make a habit of asking yourself what you’re assuming and why you’re assuming that, and check for things like stereotyping.

Becoming more mindful of your own biases is the first step to rewriting these parts of your thinking.

4. Adjust Your Perspective

Good critical thinkers do their best to be neutral with respect to their own beliefs and thoughts, spotting biases and prejudices and then correcting them.

How to Improve:  Think of yourself as a judge. You have to evaluate both sides of an argument, but you also need to keep in mind the biases each side may possess.

When evaluating information, ask yourself theses questions:

• Who will this benefit?
• Does the source have an agenda?
• Is the source ignoring, overlooking or leaving out information?
• Is this source using unnecessary language to change an audience’s mind?

5. Establish Foresight

Critical thinking is heavily dependent on problem-solving. An effective critical thinker will have the foresight to anticipate roadblocks and negative outcomes. You’ll be able to make the right decisions if you can already see the consequences down the line.

How to Improve:  Making a pro and con list is a great way to boost foresight, making you better at predicting outcomes. The more you do this, the less work you need to put into your attempted predictions each time.

6. Be an Independent Thinker

“Too often we enjoy the comfort of opinion without the discomfort of thought” — JFK

Don’t get hung up in research, reading, others’ opinions that you forget to think for yourself.

You must learn to think for yourself and take ownership of your own values, beliefs, judgments, and decisions.

How to Improve:  One of the best ways to avoid mental laziness is to practice the 15-minute rule — a process you can apply when you get stuck:

• When you’re presented with a problem you don’t immediately know the answer to, ask if you have even a small amount of confidence to solve it on your own
• If yes, then spend 15 minutes trying to solve the problem
• Document each step of your process
• If you still haven’t solved the problem after 15 minutes, get help (from a person or Google).

Over time, this process will help you build your critical thinking skills and self-reliance.

I hope you found this article resourceful and are able to use some of it to help you think more critically in your career and your life.

If you approach critical thinking skills as a process to follow, you’ll consistently heighten your thought process and when utilized regularly, it will become a habit and improve your critical thinking skills over time.

Remember:  learning to think critically is a never-ending journey, there is always more to learn, assess and improve.

Mansi Baguant: Graduate Data Scientist at Arup

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Data Science Thinking

• First Online: 21 March 2023

Cite this chapter

• Orit Hazzan   ORCID: orcid.org/0000-0002-8627-0997 3 &
• Koby Mike   ORCID: orcid.org/0000-0002-0977-9845 3  

1121 Accesses

This chapter highlights the cognitive aspect of data science. It presents a variety of modes of thinking, which are associated with the different components of data science, and describes the contribution of each one to data thinking—the mode of thinking required of data scientists (not only professional ones). Indeed, data science thinking integrates the thinking modes associated with the various disciplines that make up data science. Specifically, computer science contributes computational thinking (Sect.  3.2.1 ), statistics contributes statistical thinking (Sect.  3.2.2 ), mathematics adds different ways in which data science concepts can be conceived (Sect.  3.2.3 ), and each application domain brings with it its thinking skills, core principles, and ethical considerations (Sect.  3.2.4 ). Finally, based on these thinking modes, which are associated with the components of data science, we present data thinking (Sect.  3.2.5 ). The definition of data science inspires the message that processes of solving real-life problems using data science methods should not be based only on algorithms and data, but also on the application domain knowledge. In Sect.  3.3 we present a set of exercises that analyze the thinking skills associated with data science.

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This section is partially based on Chap. 4 of Hazzan et al. ( 2020 ). Presented here with permission.

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Hazzan, O., Mike, K. (2023). Data Science Thinking. In: Guide to Teaching Data Science. Springer, Cham. https://doi.org/10.1007/978-3-031-24758-3_3

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Sharpening Your Analytical Edge: The Importance of Critical Thinking in Data Science

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As the digital age continues to evolve, the role of data science becomes increasingly significant.

Critical thinking in data science – which is the ability to analyse and interpret complex data sets, a highly sought-after skill in many industries – is more important than ever.

When critical thinking and technical expertise come together, the true power of data science is unlocked.

This article explores the importance of sharpening your analytical edge and the role of critical thinking in data science.

The intersection of data science and critical thinking

Data science is a field that combines domain expertise, knowledge of mathematics and statistics , and programming skills to extract meaningful insights from data.

It involves a multitude of processes , including data collection, data cleaning, data analysis, and data interpretation.

However, these technical aspects are only one side of the coin.

The other side is critical thinking, the ability to critically evaluate an issue to form an objective judgement.

We find the analytical edge at the intersection of data science and critical thinking.

Critical thinking in data science is the ability to understand and manipulate data, question assumptions, evaluate evidence, and make informed decisions based on the data at hand.

In other words, the analytical edge allows data scientists to turn raw data into actionable insights.

Critical thinking in data science: why it matters

Critical thinking in data science is essential.

After all, isn’t it enough to be proficient in programming languages like Python or R and to have a solid understanding of statistics?

While these skills are undoubtedly important, they must be sufficient.

Without critical thinking in data science, a data scientist might quickly draw incorrect conclusions from the data or fail to notice essential patterns or trends.

Furthermore, critical thinking in data science is essential for dealing with data’s inherent uncertainty and ambiguity.

Data is rarely clean or complete and often contains errors or inconsistencies.

Critical thinking in data science means recognising and considering these issues when analysing the data.

Critical thinking in data science leads to more reliable and accurate results and, ultimately, to better decision-making.

How to sharpen your analytical edge

So, how can one sharpen their analytical edge and become a better critical thinker in data science?

The first step is to develop a questioning mindset.

Critical thinking in data science involves being curious, open-minded, and sceptical.

Instead of taking things at face value, one should always ask questions and seek evidence.

For example, if a data set shows a particular trend, one should ask why it exists and what factors might influence it.

The second step is to improve one’s logical reasoning skills.

This can be done through practice and training.

Many resources available online, such as logic puzzles and games, can help one become a better logical thinker.

Additionally, it can be beneficial to study formal logic and argumentation, as these fields provide the theoretical foundation for critical thinking.

The third step is to learn how to deal with uncertainty and ambiguity.

This involves being comfortable with not knowing the answer and being able to make decisions based on incomplete or uncertain information.

One way to improve this skill is to regularly expose oneself to complex problems that need a clear or straightforward solution.

In conclusion, critical thinking in data science is a vital component.

It allows data scientists to turn raw data into meaningful insights and make better decisions based on these insights.

Data scientists can become more effective and valuable by sharpening their analytical edge.

So, whether you’re a seasoned data scientist or just starting in the field, remember to always question and analyse because critical thinking in data science matters.

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More From Forbes

Critical thinking: a must for maintaining data integrity.

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Gilda is the founder and CEO of technology solutions and compliance consulting services company PQE Group .

In today’s world, companies in the pharmaceutical manufacturing sector and beyond have to become increasingly aware of the scrutiny of their data. Due to rapidly evolving business models, globalization, complex and interdependent supply chains, technology and more, data integrity has emerged as one of the most important concepts to ensure trustworthiness, transparency and quality.

In a recent statement from The World Health Organization , it’s mentioned that data integrity is “a scientific necessity and an ethical must. Data must be robust, exhaustive and verifiable, through peer review. Data integrity is priceless.” I not only agree with this statement, but I also believe that using the power of critical thinking is one of the best tools to maintain data integrity for several key reasons.

What is critical thinking?

When trying to define critical thinking, there’s no single definition that fits everything it encompasses or can be used for. However, Oxford Dictionary states it as “the objective analysis and evaluation of an issue in order to form a judgment,” and this can relate to data integrity and knowledge management as well. It is crucial to remain intellectually disciplined and self-aware to obtain the most balanced and true results.

Any proper analysis of data involves turning complex information into insights. Critical thinking is a powerful tool to help guide us in asking the right questions and looking at recorded numbers from a wider variety of perspectives. This insightful article on data science makes the point that “without critical thinking, it’s easy for us to be manipulated and for all kinds of disasters to result.”

This is especially true when it comes to maintaining data integrity since we should always remain vigilant in asking the right questions and examining our own thought processes to ensure we aren’t misinterpreting what’s in front of us.

Use critical thinking to evaluate risk.

A core component of critical thinking as it relates to data integrity is that this process can help us properly see and act on risk. When you take a risk-based approach to data, you’re inherently using critical thinking skills to proactively address things that are “too good to be true.” Critical thinking leads us to ask ourselves about what can go wrong while encouraging us to proactively set out to address those problems.

Critical thinking primarily assists us in identifying risk more rapidly, and it can be a useful supplement for pharmaceutical manufacturers when paired with good software engineering principles and other industry best practices. Using the two together is a holistic approach that can efficiently manage data integrity risks to support a product’s overall quality while also ensuring patient safety.

Critical thinking begins with our own mindset.

No matter how much technology evolves, humans still play an active and important role in using these tools safely and effectively. The quality and integrity of the data we collect ultimately depend on us, and that’s where critical thinking becomes indispensable. A good critical thinker should be objective, analytical, a problem solver and ultimately respect the facts all while keeping their own preconceived notions and opinions in check.

I believe that, along with maintaining the integrity of data, we also have a responsibility to teach those managing that data the critical thinking skills necessary to help limit bias, assumptions, emotions and preconceived notions.

There are a variety of ways to develop valuable critical thinking skills in your company, beginning with changing mindsets and encouraging discussion. I think keeping open minds and allowing for the intake of new ideas and information is the only way we can hope to attain the best possible answers to the challenges confronting us.

When faced with an obstacle, it’s vital to approach the issue objectively and assess all perspectives and possibilities. This will lessen the chance of personal bias or preconceived notions impacting the data.

Lastly, asking questions is one of the most important and influential concepts to develop excellent critical thinking skills. Evaluating and analyzing the environment around you with an inquisitive mind can prove to yield innovative solutions.

By using critical thinking in everyday decision-making, companies will be more successful in meeting the challenges of client expectations, customer needs and regulatory requirements in increasingly global markets. And as the amount of data continues to grow, so does the need for critical thinking to navigate it and ultimately produce stronger, more desirable outcomes.

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