application of critical thinking in journalism

639. Digital startup steps up when relentless storms devastate community

Photojournalist Kevin Painchaud is a member of the Lookout Santa Cruz team that won a Pulitzer Prize for Breaking News for its community-focused coverage of the flooding and mudslides that destroyed thousands of homes in early 2023.

412. Critical thinking is an essential skill for journalists

Journalists take their obligations seriously to try and discern the facts from the lies or misinformation, but that doesn’t mean only double or triple-checking sources and bits of data. 

What journalists really need are critical thinking skills, says Jonathan Haber, author of Critical Thinking , the 50th title in Essential Knowledge Series from MIT Press.

“Given the time we’re living through, where you have competing information that can have life-or-death consequences, the need to think independently and critically, rather than believe what we’re told, is more crucial than ever,” Haber says. 

Think of the world of science, with its structures and methods for taking a question and driving toward a confirmation or dismissal of a hypothesis. Science is “more of a culture, a culture of ways and ways of checking your own thinking and ways of thinking that diminishes the likelihood that you’ll believe something that’s false,” Haber says.

Journalists, like teachers and people in other skilled professions, have internalized some concepts and facets of critical thinking into their approach to work, but it’s not embraced or necessarily taught on a systematic level, Haber says. 

“Facts are the lifeblood of journalism. They’re terrific, they’re important, I’m a big fan of fact checking sites, but facts are not enough,” he says. For example, if someone wrote an editorial arguing that social distancing measures put in place to slow the spread of COVID-19 were not effective because the death toll is still rising, the facts made in the editorial are true: people have been social distancing for a month or more, and the death toll has continued to increase. 

“Those two premises are true facts . Fact checking would confirm that the facts are right,” he says. “But if you look at the conclusion, it’s clearly false. Why is it false? It’s false because the facts, the evidence doesn’t prove enough reason to believe the conclusions. Nobody promised us that social distancing would work immediately, we were told it would take time. The argument is invalid, it’s a weak argument.” 

No efforts toward fact checking are wasted, Haber stresses, but that’s only one part of the process for truly thinking critically about an argument, a statement or a policy. 

It’s All Journalism host Michael O’Connell talks to Jonathan Haber, author of a new book on critical thinking from MIT Press, about how journalists can use critical thinking to improve their reporting.

638. Newsy Nick and the next stage in journalism’s evolution

Andrew Conte, director of the Center for Media Innovation at Point Park University, and graduate student Nick Tommarello share how the Center for Media Innovation is revitalizing local journalism in Pittsburgh.

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288. Telling stories of the past to change the world’s future

Erin Harper, a multimedia producer at the U.S. Holocaust Memorial Museum, joins producer Michael O’Connell to discuss how her journalism and newsroom skills are helping to tell important stories at one of D.C.’s most powerful institutions.

454. Solutions Journalism tackles community problems

Karen Magnuson of the Solutions Journalism Network talks about its new project focusing on caregivers for the aging population.

511. Working to make newsrooms anti-racist, equitable and just

Sisi Wei is co-executive director o OpenNews, which is helping to build more equitable future for journalism. In March, OpenNews marked the one year anniversary of the DEI Coalition for anti-racist, equitable and just newsrooms.

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Expert Commentary

Guide to critical thinking, research, data and theory: Overview for journalists

Terms and concepts relating to academic research methods, theories and ways of thinking about questions.

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This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License .

by John Wihbey, The Journalist's Resource March 6, 2015

Journalists constantly face the challenge of explaining why things happened: What were the factors in an election victory? What are the reasons behind housing segregation in a city? What is the explanation for a low-performing school? In daily journalism, we are often content to quote relevant sources or officials, and let them do the “explaining.”

But great journalism can do much more than that, particularly if more rigorous thinking and methods are applied. Though journalists need not understand all of the analytical tools of academics, they can benefit from understanding how critical thinking operates in the research world — and using it to their advantage.

There are two reasons why: First, knowing the precise meaning of research-related terms such as “independent variable” or symbols such as “n” can help journalists read and evaluate important studies more effectively. (See our tip sheet on statistical terms for some of the basics, as well as tips on core methods such as regression analysis .) Second, the core journalistic enterprise of verifying information and putting it in context has strong parallels with academic research methods. Both academics and journalists are, in essence, “hypothesis testing”: Data is gathered — statistics, interviews, documents, etc. — and tentative explanations are proposed and tested to arrive at final, defensible explanations of events. Being able to reason in this rigorous way about questions can create deeper, more informed stories.

This type of critical thinking can also benefit the practice of data journalism, where the best work is showing increasing sophistication, but where non-specialists remain at high risk for errors in reasoning and inference.

This overview of academic and critical reasoning comes courtesy of Stephen Van Evera , the Ford International Professor in the MIT Political Science Department. Much of the material in expanded form can be found in his short, useful book Guide to Methods for Students of Political Science . We are grateful to him for allowing us to post an edited version of a memo that was the basis for his work:

I. WHAT IS A THEORY?

Theories are general statements that describe and explain the causes or effects of classes of phenomena. They are composed of causal laws or hypotheses, explanations, and antecedent conditions. Explanations are also composed of causal laws or hypotheses, which are in turn composed of dependent and independent variables. Fourteen definitions are worth mentioning:

1. Law: Laws are observed regular relationships between two phenomena and can be deterministic or probabilistic. The former describe invariate relationships (“If ‘A’ then always ‘B’ “), while the latter frame probabilistic relationships (“If ‘A’ then sometimes ‘B’, with probability ‘X’ “). Hard science has many deterministic laws. Nearly all social science laws are probabilistic. Laws can be causal (” ‘A’ causes ‘B’ “) or spurious (” ‘A’ and ‘B’ are caused by ‘C’; hence ‘A’ and ‘B’ are correlated but do not cause each other”). Our prime search is for causal laws. We explore the possibility that laws are spurious mainly to rule it out, so we can rule in the possibility that observed laws are causal.

2. Hypothesis: A conjectured relationship between two phenomena. Like laws, hypotheses can be causal (“I surmise that ‘A’ causes ‘B’ “) and non-causal (“I surmise that ‘A’ and ‘B’ are caused by ‘C’; hence ‘A’ and ‘B’ are correlated but do not cause each other”).

3. Theory: A causal law (“I have established that ‘A’ causes ‘B’ “) or causal hypothesis (“I surmise that ‘A’ causes ‘B’ “) and an explanation of the causal law or hypothesis that explicates how “A” causes “B.” Note that the term “general theory” is often used for more wide-ranging theories, but all theories are by definition general to some degree.

4. Explanation: The causal laws or hypotheses that connect the cause to the phenomenon being caused, showing how causation occurs. (” ‘A’ causes ‘B’ because ‘A’ causes ‘q’, which causes ‘r’, which causes ‘B’.”)

5. Antecedent condition: A phenomenon whose presence activates or magnifies the action of a causal law or hypothesis. Without it causation operates more weakly (” ‘A’ causes some ‘B’ if ‘C’ is absent, and more ‘B’ if ‘C’ is present”; e.g., “sunshine makes grass grow, but causes large growth in fertilized soil”) or not at all (” ‘A’ causes ‘B’ if ‘C’ is present, otherwise not”; e.g., “sunshine makes grass grow, but only if we also get rain”). An antecedent condition can be restated as a causal law or hypothesis. (” ‘C’ causes ‘B’ if ‘A’ is present, otherwise not”; e.g., “rain makes grass grow, but only if we also get some sunshine”). When referring to antecedent conditions, researchers often use terms such as “interaction terms,” “initial conditions,” “enabling conditions,” “catalytic conditions,” “preconditions,” “activating conditions,” “magnifying conditions,” “assumptions,” “assumed conditions,” or “auxiliary assumptions.”

6. Variable: A concept that can have various values, e.g., the “degree of democracy” in a country or the “share of the two-party vote” for a political party.

7. Independent variable (IV): A variable framing the causal phenomenon of a causal theory or hypothesis. In the hypothesis “literacy causes democracy,” the degree of literacy is the independent variable.

9. Intervening variables (IntVs): Variables framing intervening phenomena that form a causal theory’s explanation. These phenomena are caused by the independent variable and cause the dependent variable. In the theory “sunshine causes photosynthesis, causing grass to grow,” photosynthesis is the intervening variable.

10. Condition variables (CVs): Variables framing antecedent conditions. Their values govern the size of the impact that IVs or IntVs have on DVs and other IntVs. In the hypothesis “sunshine makes grass grow, but only if we also get some rainfall,” the amount of rainfall is a condition variable.

11. Study variable (SV): A variable whose causes or effects we seek to discover with our research. A project’s study variable can be an IV, DV, IntV, or CV.

12. Prime hypothesis (PH): The overarching hypothesis that frames the relationship between a theory’s independent and dependent variables.

13. Explanatory hypotheses (EH): The intermediate hypotheses that comprise a theory’s explanation.

14. Test hypothesis (TH): The hypothesis we seek to test. Also called the “research hypothesis.” Note that a theory is nothing more than a set of connected causal laws or hypotheses.

II. WHAT IS A SPECIFIC EXPLANATION?

Explanations of specific events (wars, revolutions, elections, economic depressions, etc.) use theories and are framed like theories. A good explanation tells us what specific causes produced a specific phenomenon and identifies the general phenomenon of which this specific cause is an example. Several concepts bear mention:

2. Non-generalized specific explanation: A specific explanation that does not identify the theory that the operating cause is an example of. For example, the statement “Germany caused World War II” does not answer the question of “Of what is Germany an example?”

3. Generalized specific explanation: A specific explanation that identifies the theories that govern its operation . For example, in the statement “German expansionism caused World War II,” the operating cause, “German expansionism,” is an example of expansionism, which is the independent variable in the hypothesis “expansionism causes war.”

Specific explanations are comprised of four phenomena: “Causal phenomenon” (CP), which are the cause. “Caused phenomenon” (OP) are being brought about by the casual phenomenon. “Intervening phenomena” (IP) are are caused by the causal phenomenon and cause the outcome phenomenon. And finally, “antecedent phenomena” (AP), whose presence activates or magnifies the causal action of the causal and/or explanatory phenomena.

III. WHAT IS A GOOD THEORY?

Seven prime attributes govern a theory’s quality. Good theories:

1. Have large explanatory power: The theory’s independent variable has a large effect on a wide range of phenomena under a wide range of conditions. Three characteristics govern explanatory power:

a. Importance: Does variance in the value on the independent variable cause large or small variance in the value on the dependent variable? An important theory points to a cause that has a large impact, i.e., that causes large variance on the DV. The greater the variance produced, the greater the theory’s explanatory power.

b. Explanatory range : How many classes of phenomena are affected by, hence explained by, variance in the value on the theory’s independent variable? The wider the range of affected phenomena, the greater the theory’s explanatory power. Most social science theories have narrow range but a few gems explain many diverse domains.

c. Applicability : How common is the theory’s cause in the real world? How common are antecedent conditions that activate its operation? The more prevalent the causes and conditions of the theory, the greater its explanatory power. The prevalence of these causes and conditions in the past govern its power to explain history. Their current and future prevalence govern its power to explain present and future events.

2. Elucidate by simplifying: A good theory is parsimonious , using few variables simply arranged to explain its effects. However, parsimony often requires some sacrifice of explanatory power. If that sacrifice is too large it becomes unworthwhile. We can tolerate some complexity if we need it to explain the world.

3. Are “satisfying”: A good theory satisfies our curiosity and an unsatisfying one leaves us wondering what causes the cause proposed by the theory. A politician once explained her election loss: “I didn’t get enough votes!” This is true but unsatisfying. We still want to know why she didn’t get enough votes. The farther removed a cause stands from its proposed effect, the more satisfying the theory. Thus “droughts cause famine” is less satisfying than “changes in ocean surface temperature cause shifts in atmospheric wind patterns, causing shifts in areas of heavy rainfall, causing droughts, causing famine.”

A clearly framed theory includes a statement of the antecedent conditions that enable its operation and govern its impact. Otherwise we cannot tell what cases the theory governs, and thus cannot infer useful policy prescriptions. Foreign policy disasters often happen because policymakers apply valid theories to inappropriate circumstances. Consider the hypothesis that “appeasing other states makes them more aggressive, causing war.” This was true with Germany during 1938-1939, but the opposite is sometimes true: A firm stand can make the other more aggressive, causing war. To avoid policy backfires, policymakers must know the antecedent conditions that decide if a firm stand makes others more or less aggressive. Parallel problems arise in all policymaking domains and highlight the importance of framing antecedent conditions clearly.

5. Are in principle “falsifiable”: Theories that are not clearly framed may prevent investigators from inferring predictions from them. Theories that make omni-predictions that are fulfilled by all observed events also are non-falsifiable. Empirical tests cannot corroborate or infirm such theories because all evidence is consistent with them. Religious theories of phenomena have this quality: happy outcomes are God’s reward, disasters are God’s punishment, cruelties are God’s tests of our faith, and outcomes that elude these broad categories are God’s mysteries.

6. Explain important phenomena: A good theory answers questions that matter to the wider world, or it helps others answer such questions. Theories that answer unasked questions are less useful even if they answer these questions well. (Much social science theorizing has little real-world relevance.)

7. Have prescriptive richness: A good theory yields useful policy recommendations. A theory gains prescriptive richness by pointing to manipulable causes, since manipulable causes might be controlled by human action. Thus “capitalism causes imperialism, causing war” is less useful than “offensive military postures and doctrines cause war,” even if both theories are equally valid, because the structure of national economies is less manipulable than national military postures and doctrines. “Teaching chauvinist history in school causes war” is even more useful, since the content of national education is more easily adjusted than national military policy. A theory gains prescriptive richness by identifying dangers that could, with warning, be defeated or mitigated by timely countermeasures. Thus theories explaining the causes of hurricanes provide no way to prevent them, but they do help forecasters warn threatened communities.

IV. HOW CAN THEORIES BE MADE?

Nine aids to theory-making bear mention. (The first eight are inductive methods, the last is deductive.)

1. Examine “outliers”: Cases that are poorly explained by existing theories may have some unknown cause. To make a new theory we select cases where the phenomenon we seek to explain is abundant but its known causes are scarce or absent. Unknown causes will announce themselves as unusual characteristics of the case, and as phenomena that are associated with the dependent variable within the case. We also cull the views of people who experienced the case or know it well and nominate their explanations as candidate causes. To infer a theory’s antecedent conditions (CVs) we select cases where the DV’s causes are abundant but the DV is scarce or absent. This suggests that unknown antecedent conditions are absent in the case.

2. “Method of difference” and “method of agreement”: In the first, the analyst compares cases with similar background characteristics and different values on the study variable (i.e., the variable whose causes or effects we seek to discover), looking for other differences between cases. These other cross-case differences are nominated as possible causes of the study variable (if we seek to discover its causes) or possible effects (if we seek its effects). Similar cases are picked to reduce the number of candidate causes or effects that emerge: more similar cases produce fewer candidates, making real causes and effects easier to spot. In the method of agreement, the analyst explores cases with different characteristics and similar values on the study variable, looking for other similarities between the cases, and nominating these similarities as possible causes or effects of the variable.

3. Select cases with high or low study variable (SV) values: If values on the SV are very high (i.e., the SV phenomenon is present in abundance) its causes and effects should also be present in unusual abundance, standing out against the case background. If values on the SV are very low (i.e., the SV phenomenon is absent) its causes and effects should also be prominent by their absence.

4. Select cases with extreme within-case variance in the study variable: If values on the SV vary sharply, phenomena that co-vary with the SV should also vary sharply, standing out against the more static case background.

5. Counterfactual analysis : The analyst examines history, trying to “predict” how events would have unfolded had a few elements of the story been changed, with a focus on varying conditions that seem important and/or manipulable. For instance, to explore the effects of military factors on the likelihood of war, one might ask: “How would pre-1914 diplomacy have evolved if the leaders of Europe had not believed that conquest was easy?” Or, to explore the importance of broad social and political factors in causing Nazi aggression: “How might the 1930s have unfolded had Hitler died in 1932?” The greater the changes that one’s analysis suggests would have followed from the changes posited, the more important one’s analysis. When analysts discover counterfactual analyses they find persuasive, they have found theories they find persuasive, since all counterfactual predictions rests on theories.

6. Infer theories based on policy debates: Proponents of given policies frame specific cause-effect statements (“If communism triumphs in Vietnam, it will triumph in Thailand, Malaysia and elsewhere”) that can be framed as general theories (“Communist victories are contagious: communist victory in one state raises the odds on communist victory in others”) that can be tested. Such tests in turn can help resolve the policy debate. Theories inferred in this fashion are sure to have policy relevance and they merit close attention.

7. Seek insights from actors or observers: Those who experience an event often observe important data that are unrecorded and thus lost to later investigators. Hence they can suggest hypotheses that could not be inferred from direct observation alone.

8. Explore large-n data sets: Discovered correlations are nominated as possible cause-effect relationships. This method is seldom fruitful, however. A new large-n data set is usually hard to assemble, but if we rely on existing data sets our purview is narrowed by the curiosities of previous researchers. We can only explore theories that use variables that others have already chosen to code.

9. Adapt theories from another domain: Students of misperception in international relations and students of mass political behavior have both borrowed theories from psychology. Students of military affairs have borrowed theories from the study of organizations. Students of international systems have borrowed theories (e.g., oligopoly theory) from economics.

V. HOW CAN THEORIES BE TESTED?

1. Experimentation: An investigator infers predictions from a theory. Then the investigator exposes one of two equivalent groups to a stimulus while not exposing the other group. Are results congruent or incongruent with the predictions? Congruence of prediction and result corroborates the theory, incongruence infirms it.

2. Observation: An investigator infers predictions from a theory, then observes the data without imposing an external stimulus on the situation, and asks if observations are consistent with predictions. Two types of observational analysis can be performed:

a. Large-n, or “statistical,” analysis : A large number of cases — usually several dozen or more — is assembled and explored to see if variables shift as the theory predicts.

b. Case study analysis : A small number of cases (as few as one) are explored in detail, to see if events unfold in the manner predicted and (if the subject involves human behavior) if actors speak and act as the theory predicts.

Which method — experiment, observation large-n, or observation case study — is best? Some hard sciences (chemistry, biology, physics) rely largely on experiments. Others (astronomy, geology, paleontology) rely largely on observation.

In political science experiments are seldom feasible, with rare exceptions (e.g. conflict simulations or psychology experiments), leaving observation as the prime method of testing. Large-n methods are relatively effective for testing theories of American electoral politics because very large numbers of cases (of elections, or of interviewed voters) are well-recorded. Case studies can be strong tools for exploring American politics, especially if in-depth case studies yield important data that is otherwise inaccessible.

VI. STRONG VS. WEAK TESTS; PREDICTIONS AND TESTS

Strong tests are preferred because they convey more information and carry more weight than weak tests. A strong test is one whose outcome is unlikely to result from any factors except the operation or failure of the theory. Strong tests evaluate predictions that are certain and unique : A certain prediction is an unequivocal forecast, and the more certain, the stronger the test. A unique prediction is a forecast not made by other known theories, and the more unique, the stronger the test. The most unique predictions forecast outcomes that could have no plausible cause except the theory’s action.

Certainty and uniqueness are both matters of degree. Tests of predictions that are highly certain and highly unique are strongest, since they provide decisive positive and negative evidence. As the degree of certitude or uniqueness falls, the strength of the test also falls. Tests of predictions that have little certitude or uniqueness are weakest, and are worthless if the tested prediction has no certitude or uniqueness.

There are four types of tests, differing by their combinations of strength and weakness:

1. Hoop tests. Predictions of high certitude and no uniqueness provide decisive negative tests: a flunked test kills a theory or explanation, but a passed test gives it little support. For example: “Was the accused in town on the day of the murder?” If not, he’s innocent, but showing that he was in town does not prove him guilty. To remain viable the theory must jump through the hoop this test presents, but passage of the test still leaves the theory in limbo.

3. Doubly-decisive tests. Predictions of high uniqueness and high certitude provide tests that are decisive both ways: passage strongly corroborates an explanation, a flunk kills it. If a bank security camera records the faces of bank robbers, its film is decisive both ways — it proves suspects guilty or innocent. Such tests combine both a “hoop test” and “smoking gun test” and convey the most information, but are rare.

4. Straw-in-the-wind tests. Most predictions have low uniqueness and low certitude are indecisive both ways: Passed and flunked tests provide straws in the wind but are themselves indecisive. Thus many explanations for historical events make probabilistic predictions (“If Hitler ordered the Holocaust, we should probably find some written record of his orders”), whose failure may simply reflect the downside probabilities. We learn something by testing such straw-in-the-wind predictions, but such tests are never decisive by themselves. Unfortunately, this describes the predictions we usually work with.

Strong tests are preferred to weak tests, but tests can also be hyper-strong, i.e., unfair to the theory. For example, one can perform tests under conditions where countervailing forces are present that counteract its predicted action. Passage of such tests is impressive because it shows the theory’s cause has large importance, i.e., high impact. However, a valid theory may flunk such tests because the countervailing factor masks its action. Such a test misleads by recording a false negative — unless the investigator, mindful of the test’s bias, gives the theory bonus points for the extra hardship it faces.

VIII. HOW CAN SPECIFIC EVENTS BE EXPLAINED?

1. Does the explanation exemplify a valid general theory? To assess the hypothesis that “a” caused “b” in a specific instance, we first assess the hypothesis’ general form (” ‘A’ causes ‘B’ “). If “A” does not cause “B,” we can rule out all explanations of specific instances of “B” that assert that examples of “A” were the cause, including the hypothesis that ‘a’ caused ‘b’ in this case. The argument that “the rooster’s crows caused today’s sunrise” is assessed by asking whether, in general, roosters cause sunrises by their crowing. If the hypothesis that “rooster crows cause sunrises” has been tested and flunked, we can infer that the rooster’s crow cannot explain today’s sunrise. The explanation fails because the covering law is false.

Generalized specific explanations are preferred to non-generalized specific explanations because we can measure the conformity of the former but not the latter with their covering laws. (The latter leave us with no identified covering laws to evaluate.) Non-generalized specific explanations must be re-cast as generalized specific explanations before we can measure this conformity.

2. Is causal phenomenon present in the case we seek to explain? A specific explanation is plausible only if the value on the independent variable of the general theory on which the explanation rests is greater than zero. Even if “A” is a confirmed cause of “B,” it cannot explain instances of “B” that occur when “A” is absent. Even if economic depressions have been shown to cause war, this theory does not explain wars that occur in periods of prosperity. Asteroid impacts may cause extinctions, but cannot explain extinctions that occurred in the absence of an impact.

3. Are the covering law’s antecedent conditions met in the case? Theories cannot explain outcomes in cases that omit their necessary antecedent conditions. Dog bites spread rabies if the dog is rabid; bites by a non-rabid dog cannot explain a rabies case.

This fourth step is necessary because the first three steps are not definitive. If we omit step four it remains possible that the covering law that supports our explanation is probabilistic and the case at hand is among those where it did not operate. We also should test the explanation’s within-case predictions as a hedge against the possibility that our faith in the covering law is misplaced, and that the “law” is in fact false. For these two reasons, the better the details of the case conform to the detailed within-case predictions of the explanation the stronger the inference that the explanation explains the case.

Analysts are allowed to infer the covering law that underlies the specific explanation of a given event from the event itself. The details of the event suggest a specific explanation; that explanation is then framed in general terms that allow tests against a broader database; these tests are passed; and the theory is then re-applied to the specific case. Thus general theory-testing and specific case-explaining can be done together and can support each other.

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Critical Thinking: What Do You Mean by That?

My question is: just what do they really mean by that?

For some, it means being skeptical — the time-honored school of “If your mother says she loves you, check it out.”  That’s a start at critical thinking.

For other news managers, I fear it simply means: “Have the ability to read my mind and know exactly how I would do the story.” That’s pretty much the end of critical thinking. It is just emulation.

So what really constitutes critical thinking? I did some checking. According to the Center for Critical Thinking , it is (now, stay with me here):

The intellectually disciplined process of actively and skillfully conceptualizing, applying, analyzing, synthesizing, and/or evaluating information gathered from, or generated by, observation, experience, reflection, reasoning, or communication, as a guide to belief and action.

Try shouting that to your reporter as she heads out the door on a challenging story.

Let’s look more closely. Much of the work of critical thinking can be broken down more simply. One team of experts broke it down into 35 individual skills . Here are some of those 35 that have great meaning for journalists:

• Thinking independently
• Exercising fairmindedness
• Exploring thoughts underlying feelings and feelings underlying thoughts
• Refining generalizations and avoiding simplifications
• Clarifying issues, conclusions, or beliefs
• Clarifying and analyzing the meanings of words and phrases
• Developing criteria for evaluation; clarifying values and standards
• Evaluating the credibility of sources of information
• Analyzing or evaluating arguments, interpretations, beliefs or theories
• Generating or assessing solutions
• Reading critically
• Listening critically
• Examining or evaluating assumptions
• Distinguishing relevant from irrelevant facts
• Making plausible inferences, predictions or interpretations

As I read that list, it reminded me how important those skills are to the role of the journalist. I also realized that the list demonstrates the difference between serious reporting and the infotainment twist-and-shout delivered via some cable news programs, talk radio shows and blogs, where critical thinking is missing in action.

As leaders, how can we talk about important critical thinking skills to our own staff members? How can we take those lofty-sounding skills and values and turn them into our daily conversation?

I think we can do it through questions, which can be far more powerful than statements.

To that end, I offer some questions that could help encourage critical thinking in the newsroom:

• What assumptions do you have about the issues or individuals in your story?
• Is there another plausible explanation we should look at in this case?
• Have you worked as hard to disprove as to prove your hypothesis?
• What’s missing here? Are we asking the right questions?
• Are we really challenging ourselves? Are we falling in love with our story as we see it now?
• Why is this information relevant to the story?
• Are we assuming that correlation is causation?
• How closely did we listen to all voices?
• Are we letting fear drive our thinking?
• Just because it has always been done this way before, is that a reason not to question?
• Just because we had a difficult time getting this information, does it make it particularly important or interesting?
• Do we have the expertise in this subject to form an appropriate conclusion? Is there more we need to learn?
• Are we oversimplifying or overgeneralizing?
• How’s our logic? Are we falling prey to ad hominem arguments (personal attacks) or false dichotomies (mistaken belief that there are only these two options) instead of examining the issue more completely?
• Is this really fair?

As newsroom leaders pray that their staffs develop better critical thinking skills, they should be prepared to fully embrace another aspect of it: independence and courage. That means your critical thinkers will be more likely to question authority. 

They’ll step up and challenge YOU. Are you ready for that?

If you truly value a culture of critical thinking, you’ll welcome those questions, too.

Think about it.

One of the key critical thinking — as well as leadership — skills is LISTENING. If listening is a challenge for you or someone on your staff, we have help. Go to NewsU.org , the free online learning center for journalists. You can take a fun and interactive course called “Lousy Listeners” that I helped create. Please let me know what you think of it!

Opinion | New books from legendary reporter Bob Woodward, PolitiFact founder Bill Adair launch today

And more media news, tidbits and interesting links for your Tuesday morning

Opinion | The big split persists in how national news outlets call presidential elections

The 2016 election shattered the status quo in election results reporting. 2 blocs of news outlets now offer competing methods — with similar findings.

Are China and Russia amplifying hurricane misinformation? Early signs say yes

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A changing COVID-19 virus means reliable vaccine information is important to audiences

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Mastering Critical Thinking: A Journalist’s Guide

• Published: December 11, 2023
• By: Yellowbrick

Journalist’s Guide to Critical Thinking

In the fast-paced world of journalism, critical thinking is an essential skill that sets apart exceptional journalists from the rest. With information constantly bombarding us from various sources, it’s crucial for journalists to be able to analyze, evaluate, and interpret information effectively. By honing their critical thinking skills, journalists can uncover hidden biases, identify misleading information, and present a well-rounded and accurate account of events. In this journalist’s guide to critical thinking, we will explore the importance of critical thinking in journalism and provide practical tips and strategies to enhance this skill.

1. Question Everything

One of the fundamental principles of critical thinking is to question everything. As a journalist, it’s essential to approach every piece of information with a healthy dose of skepticism. Don’t take things at face value; dig deeper, ask probing questions, and challenge assumptions . By doing so, you can uncover hidden agendas, biases, and inaccuracies that may be present in the information you receive.

2. Verify Sources

In the age of social media and instant news, misinformation spreads like wildfire. As a journalist, it’s your responsibility to verify the credibility of your sources before publishing any information. Double-check facts, cross-reference information with multiple sources, and ensure that you are relying on reputable sources. Remember, accuracy is paramount in journalism, and your audience relies on you to provide them with reliable information.

3. Evaluate Arguments

Critical thinking involves evaluating arguments and claims objectively. When presented with different perspectives or conflicting information, take the time to analyze each side of the argument. Consider the evidence, assess the logic and reasoning, and weigh the credibility of the sources involved. By critically evaluating arguments, you can present a balanced and well-informed account of the issue at hand.

4. Recognize Biases

Bias is an inherent part of human nature, and journalists are not exempt from it. However, being aware of your own biases and recognizing them is crucial in maintaining journalistic integrity. Additionally, learn to identify biases in the information you receive. Look out for loaded language, selective reporting, or skewed narratives that may indicate a bias. By acknowledging and addressing biases, you can strive to present a fair and unbiased account of the news.

5. Analyze Data and Statistics

In today’s data-driven world, journalists often encounter complex data and statistics. To effectively report on data, it’s essential to have strong analytical skills. Learn to interpret data accurately, understand statistical concepts, and question the validity of the data you come across. By analyzing data critically, you can provide your audience with meaningful insights and avoid misrepresenting information.

6. Consider Ethical Implications

Critical thinking in journalism goes beyond evaluating information; it also involves considering the ethical implications of your work. When making editorial decisions, think about the potential impact on individuals or communities involved. Consider the ethical standards of journalism, such as accuracy, fairness, and minimizing harm. By incorporating ethical considerations into your critical thinking process, you can uphold the integrity of your work.

7. Stay Curious and Open-Minded

To excel in critical thinking, journalists must cultivate a curious and open-minded attitude. Embrace new ideas, seek out diverse perspectives, and be willing to challenge your own beliefs. By constantly learning and expanding your knowledge, you can enhance your critical thinking skills and become a well-rounded journalist.

Key Takeaways:

• Critical thinking is essential for journalists to analyze, evaluate, and interpret information effectively.
• Question everything and approach information with skepticism.
• Verify the credibility of sources before publishing information.
• Evaluate arguments objectively, considering evidence and credibility.
• Recognize and address biases, both personal and in the information received.
• Develop strong analytical skills to interpret and analyze data accurately.
• Consider the ethical implications of editorial decisions.
• Cultivate a curious and open-minded attitude to continually learn and grow as a journalist.

To further enhance your critical thinking skills and excel in the field of journalism, consider taking the NYU | Modern Journalism online course and certificate program offered by Yellowbrick. This comprehensive program will provide you with the knowledge and tools to navigate the ever-changing media landscape with confidence. Start your journey towards becoming a skilled and insightful journalist by enrolling in the “NYU | Modern Journalism” course today.

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• What Is Critical Thinking? | Definition & Examples

What Is Critical Thinking? | Definition & Examples

Published on May 30, 2022 by Eoghan Ryan . Revised on May 31, 2023.

Critical thinking is the ability to effectively analyze information and form a judgment .

To think critically, you must be aware of your own biases and assumptions when encountering information, and apply consistent standards when evaluating sources .

Critical thinking skills help you to:

• Identify credible sources
• Evaluate and respond to arguments
• Assess alternative viewpoints
• Test hypotheses against relevant criteria

Table of contents

Why is critical thinking important, critical thinking examples, how to think critically, other interesting articles, frequently asked questions about critical thinking.

Critical thinking is important for making judgments about sources of information and forming your own arguments. It emphasizes a rational, objective, and self-aware approach that can help you to identify credible sources and strengthen your conclusions.

Critical thinking is important in all disciplines and throughout all stages of the research process . The types of evidence used in the sciences and in the humanities may differ, but critical thinking skills are relevant to both.

In academic writing , critical thinking can help you to determine whether a source:

• Is free from research bias
• Provides evidence to support its research findings
• Considers alternative viewpoints

Outside of academia, critical thinking goes hand in hand with information literacy to help you form opinions rationally and engage independently and critically with popular media.

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Critical thinking can help you to identify reliable sources of information that you can cite in your research paper . It can also guide your own research methods and inform your own arguments.

Outside of academia, critical thinking can help you to be aware of both your own and others’ biases and assumptions.

Academic examples

However, when you compare the findings of the study with other current research, you determine that the results seem improbable. You analyze the paper again, consulting the sources it cites.

You notice that the research was funded by the pharmaceutical company that created the treatment. Because of this, you view its results skeptically and determine that more independent research is necessary to confirm or refute them. Example: Poor critical thinking in an academic context You’re researching a paper on the impact wireless technology has had on developing countries that previously did not have large-scale communications infrastructure. You read an article that seems to confirm your hypothesis: the impact is mainly positive. Rather than evaluating the research methodology, you accept the findings uncritically.

Nonacademic examples

However, you decide to compare this review article with consumer reviews on a different site. You find that these reviews are not as positive. Some customers have had problems installing the alarm, and some have noted that it activates for no apparent reason.

You revisit the original review article. You notice that the words “sponsored content” appear in small print under the article title. Based on this, you conclude that the review is advertising and is therefore not an unbiased source. Example: Poor critical thinking in a nonacademic context You support a candidate in an upcoming election. You visit an online news site affiliated with their political party and read an article that criticizes their opponent. The article claims that the opponent is inexperienced in politics. You accept this without evidence, because it fits your preconceptions about the opponent.

There is no single way to think critically. How you engage with information will depend on the type of source you’re using and the information you need.

However, you can engage with sources in a systematic and critical way by asking certain questions when you encounter information. Like the CRAAP test , these questions focus on the currency , relevance , authority , accuracy , and purpose of a source of information.

When encountering information, ask:

• Who is the author? Are they an expert in their field?
• What do they say? Is their argument clear? Can you summarize it?
• When did they say this? Is the source current?
• Where is the information published? Is it an academic article? Is it peer-reviewed ?
• Why did the author publish it? What is their motivation?
• How do they make their argument? Is it backed up by evidence? Does it rely on opinion, speculation, or appeals to emotion ? Do they address alternative arguments?

Critical thinking also involves being aware of your own biases, not only those of others. When you make an argument or draw your own conclusions, you can ask similar questions about your own writing:

• Am I only considering evidence that supports my preconceptions?
• Is my argument expressed clearly and backed up with credible sources?
• Would I be convinced by this argument coming from someone else?

If you want to know more about ChatGPT, AI tools , citation , and plagiarism , make sure to check out some of our other articles with explanations and examples.

• ChatGPT vs human editor
• ChatGPT citations
• Is ChatGPT trustworthy?
• Using ChatGPT for your studies
• What is ChatGPT?
• Chicago style
• Paraphrasing

 Plagiarism

• Types of plagiarism
• Self-plagiarism
• Avoiding plagiarism
• Academic integrity
• Consequences of plagiarism
• Common knowledge

Critical thinking refers to the ability to evaluate information and to be aware of biases or assumptions, including your own.

Like information literacy , it involves evaluating arguments, identifying and solving problems in an objective and systematic way, and clearly communicating your ideas.

Critical thinking skills include the ability to:

You can assess information and arguments critically by asking certain questions about the source. You can use the CRAAP test , focusing on the currency , relevance , authority , accuracy , and purpose of a source of information.

Ask questions such as:

• Who is the author? Are they an expert?
• How do they make their argument? Is it backed up by evidence?

A credible source should pass the CRAAP test  and follow these guidelines:

• The information should be up to date and current.
• The author and publication should be a trusted authority on the subject you are researching.
• The sources the author cited should be easy to find, clear, and unbiased.
• For a web source, the URL and layout should signify that it is trustworthy.

Information literacy refers to a broad range of skills, including the ability to find, evaluate, and use sources of information effectively.

Being information literate means that you:

• Know how to find credible sources
• Use relevant sources to inform your research
• Understand what constitutes plagiarism
• Know how to cite your sources correctly

Confirmation bias is the tendency to search, interpret, and recall information in a way that aligns with our pre-existing values, opinions, or beliefs. It refers to the ability to recollect information best when it amplifies what we already believe. Relatedly, we tend to forget information that contradicts our opinions.

Although selective recall is a component of confirmation bias, it should not be confused with recall bias.

On the other hand, recall bias refers to the differences in the ability between study participants to recall past events when self-reporting is used. This difference in accuracy or completeness of recollection is not related to beliefs or opinions. Rather, recall bias relates to other factors, such as the length of the recall period, age, and the characteristics of the disease under investigation.

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Ryan, E. (2023, May 31). What Is Critical Thinking? | Definition & Examples. Scribbr. Retrieved October 15, 2024, from https://www.scribbr.com/working-with-sources/critical-thinking/

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