Critical Thinking and Persuasive Reasoning: A Practical Guide - Chapter 2: Two Paths to Reasoning: Deductive and Inductive Thinking

The Basics of Reasoning

At the heart of all critical thinking lies a simple question: how do we know what we know? Whether we’re making a personal decision, crafting an argument, or evaluating someone else’s claim, we rely on reasoning to draw conclusions. But not all reasoning works the same way. In fact, most of the arguments we encounter fall into one of two major categories: deductive reasoning and inductive reasoning. These are not just academic labels—they are the mental tools we use to navigate daily life, whether we’re aware of them or not.

Deductive reasoning is about certainty. It starts with general principles and uses them to reach specific, logically guaranteed conclusions. If the premises are true and the structure is valid, then the conclusion must also be true (Moore & Parker, 2017). In contrast, inductive reasoning is about probability. It moves from specific observations to broader generalizations. Inductive arguments are not guaranteed to be true, but they can be highly persuasive and practically useful—especially in science, media, and everyday decision-making.

Understanding the differences between these two forms of reasoning is essential for evaluating arguments. But it’s also the key to becoming more precise in your own thinking. In this chapter, we’ll break down both types of reasoning, explore their structures, examine common pitfalls, and show how they shape the arguments that influence our world.

Understanding Deductive Reasoning

Start by asking yourself: When was the last time I changed my mind about something important? What made me reconsider? If you can’t think of an example, that might be a sign that you’re surrounding yourself with people and information that always agree with you. Growth often begins at the edge of our comfort zones, and being open to change is a sign of intellectual humility—not weakness. A classic example of deductive reasoning is the following:

All humans are mortal.
Socrates is a human.
Therefore, Socrates is mortal.
(Moore & Parker, 2017)

This is a valid and sound deductive argument. It is valid because the structure guarantees the conclusion, and it is sound because the premises are also true. When both of those conditions are met—validity and truth—the conclusion is inescapable.

To understand how deductive reasoning works, it's helpful to break it down into three components: premises, logic, and conclusion. The premises are the statements you begin with; the logic is the structure that connects them; and the conclusion is the result that follows. If the structure is flawed—say, if it commits a formal fallacy—then the conclusion may be unreliable, even if the premises seem persuasive.

But deductive reasoning doesn’t only exist in philosophical thought experiments. It shows up in law, math, science, and everyday problem-solving. For instance, if a workplace rule says, “Any employee who is late three times will lose their bonus,” and if Angela has already been late three times, then the conclusion that she will lose her bonus logically follows. Whether or not we like the result, the logic is airtight—assuming the policy is applied consistently.

However, not all deductive arguments are this straightforward. Some are dressed up in ordinary language and can be harder to spot. Consider the statement: “If this medicine doesn’t work, I’ll feel exactly the same. I feel better. Therefore, the medicine must have worked.” This seems reasonable at first glance, but it’s actually an example of faulty deductive reasoning. It uses a false premise or a problematic assumption that there were no other factors involved. Maybe rest, time, or placebo effects were at play. Deductive reasoning is only reliable when the premises are both logically structured and factually accurate.

A critical part of mastering deductive reasoning is understanding the concept of conditional (if-then) statements. These are the foundation of many deductive arguments. For example:

If it rains, the ground will be wet.
It is raining.
Therefore, the ground will be wet.

This type of argument follows a structure called modus ponens, which is Latin for “affirming the antecedent.” It’s a valid deductive form (Govier, 2018). There’s also modus tollens, or “denying the consequent,” which looks like this:

If it rains, the ground will be wet.
The ground is not wet.
Therefore, it is not raining.

Both forms are logically valid because the structure holds up no matter what topic you plug into it.

But beware of structures that only appear logical. For instance:

If it rains, the ground will be wet.
The ground is wet.
Therefore, it rained.

This argument affirms the consequent, a formal fallacy. The ground could be wet for other reasons—a sprinkler, a spilled drink, or melting snow (Walton, 2008). The argument looks deductive but doesn't guarantee the conclusion, so it fails the test of logical certainty.

Learning to identify the form of a deductive argument can help you separate clear thinking from persuasive-sounding nonsense. Politicians, advertisers, and even well-meaning friends may use faulty deduction to persuade us. A critical thinker learns to pause and ask: Are the premises true? Is the logic valid? Does the conclusion actually follow?

In legal settings, deductive reasoning forms the backbone of argumentation. For example, in a courtroom, lawyers might argue, “According to the statute, any person found trespassing on government property without authorization is guilty of a misdemeanor. The defendant was found trespassing without authorization. Therefore, the defendant is guilty of a misdemeanor.” This argument relies on clear definitions, consistent application of rules, and airtight logic—hallmarks of deductive reasoning.

In academic writing, deductive reasoning helps writers build persuasive essays that move from thesis statements to conclusions supported by clear, consistent evidence. A strong paper doesn’t just collect facts—it arranges them so that each claim logically supports the next.

And in our personal lives, deductive reasoning can help us make firm, fair decisions. For instance, if you’ve decided not to lend money to friends who haven’t repaid you in the past, and someone who fits that description asks again, applying your principle consistently may help you avoid emotional decision-making. That doesn’t mean you can’t choose to make an exception—but if you do, you’ll know you’re basing it on a conscious value, not on fuzzy reasoning.

In summary, deductive reasoning is a tool for achieving certainty when we start with reliable general rules. Its strength lies in its structure: when the parts are solid, the outcome is solid too. But it’s only as trustworthy as the foundation it’s built on. If we get lazy with our premises or careless with our logic, we lose the precision that deductive reasoning offers. That’s why critical thinkers use deduction not to win arguments, but to clarify and strengthen them.

Understanding Inductive Reasoning

While deductive reasoning aims for certainty, inductive reasoning deals with probability. Instead of moving from general truths to specific conclusions, inductive reasoning does the opposite—it begins with specific observations and uses them to draw broader generalizations. In this way, inductive thinking reflects how we learn from experience, how science progresses, and how we make most of our everyday decisions.

Unlike deductive arguments, which are either valid or invalid, inductive arguments fall along a spectrum of strength. Some are weak, offering little evidence to support a conclusion, while others are strong, presenting multiple examples, patterns, or statistics. But no matter how strong an inductive argument is, its conclusion is never guaranteed. It is always open to revision if new evidence appears. This openness is both its strength and its limitation.

Take this simple example:

Every time I’ve eaten at that taco truck, the food has been excellent.
Therefore, the food at that taco truck is probably excellent.

This is an inductive generalization. It’s based on repeated personal experience, and it's probably reliable—at least for now. But it’s still possible that the next time, the food might be cold or the quality might change. The conclusion is likely, not certain. Still, this is the kind of reasoning we rely on constantly: deciding which routes are fastest, which stores are overpriced, which teachers are fair, or which movies are worth watching.

Science also relies heavily on inductive reasoning (Govier, 2018). Researchers observe natural phenomena, collect data, and form hypotheses based on patterns they detect. If multiple studies find that a medication reduces symptoms in a wide sample of patients, we inductively conclude that the medication is likely effective. But science is cautious; it rarely claims absolute truth. It updates its conclusions as more data becomes available. That’s the beauty of scientific reasoning—it embraces uncertainty and improves over time.

Another common form of inductive reasoning is analogical reasoning, where we compare two similar cases and predict that what’s true in one will also be true in the other. For example:

This hybrid car model improved fuel efficiency in the city.
The new model is based on the same design.
Therefore, the new model will probably also be fuel efficient.

This argument may sound reasonable, but it depends on how strong the analogy is. If the new car has a completely different engine or body weight, the analogy weakens. Strong analogies focus on relevant similarities, not just surface-level comparisons.

A third type of inductive reasoning involves causal reasoning—figuring out what causes what. This is tricky, because correlation doesn’t always imply causation. Just because two things happen around the same time doesn’t mean one caused the other. For instance:

I wore my lucky socks, and we won the game.
Therefore, my socks helped us win.
(Tversky & Kahneman, 1974)

This is a classic example of a false cause fallacy. Critical thinkers learn to ask whether there is evidence of a direct connection or whether other factors could explain the outcome. In causal arguments, the burden of proof is especially important. Good reasoning asks: Could this be a coincidence? Is there a known mechanism linking the events?

We also use inductive reasoning in social life. When we say things like, “She always cancels at the last minute,” or “He never replies to emails,” we’re forming generalizations based on patterns of behavior. These conclusions may be fair, or they may be unfair, depending on how much evidence we have. The danger comes when we overgeneralize based on limited experience—especially when those generalizations are applied to groups. For example, if someone says, “Teenagers today are lazy,” based on a single encounter, they’re using weak inductive reasoning to draw a sweeping—and likely biased—conclusion (Kahneman, 2011).

To strengthen an inductive argument, we must consider several factors. First, the sample size matters (Moore & Parker, 2017). A conclusion based on ten examples is usually more persuasive than one based on two. Second, the representativeness of the examples is important. If we’re trying to make a general claim about college students but all of our observations come from one campus or one class, the conclusion may not apply elsewhere. Third, we must remain open to counterexamples—instances that contradict our conclusion. A strong inductive thinker adjusts their generalization as new information emerges.

In daily life, we often combine deductive and inductive reasoning without even noticing (Kahneman, 2011). We might use inductive reasoning to form a belief (“Most of the customers here look unhappy”), and then use deductive reasoning to act on it (“If customer service is bad here, I’ll avoid this place in the future”). The two forms of reasoning complement each other. Inductive thinking helps us discover patterns and form expectations; deductive thinking helps us apply principles and test the limits of our assumptions.

What makes inductive reasoning especially valuable is its flexibility. It’s not rigid—it grows with experience. It helps us stay adaptable in a changing world, open to learning from both success and failure. But this same flexibility also requires caution. Because inductive arguments are never certain, we must always be willing to revisit our conclusions and ask: Do I have enough evidence? Am I generalizing fairly? What else might be going on?

Comparing Deductive and Inductive Reasoning

By now, it’s clear that deductive and inductive reasoning serve different—but equally important—purposes. Together, they form the backbone of how we make sense of the world, how we argue, and how we solve problems. Understanding their differences helps us choose the right approach for the situation and evaluate others’ arguments with greater accuracy.

Deductive reasoning is about structure. If the premises are true and the structure is valid, the conclusion is inescapable. It is precise, rule-based, and often used in law, logic, philosophy, and formal arguments. It is especially useful when the stakes are high and decisions must follow strict rules or established principles. The downside? Deductive reasoning only works if the premises are sound. If you start with a faulty premise, even perfect logic can lead to a false conclusion.

Inductive reasoning, by contrast, is about pattern recognition and probability. It’s how we build general knowledge from experience, observe trends, or make predictions. It’s the method behind most scientific discovery, personal judgment, and public discourse. Its strength is flexibility: it can grow and shift with new information. But its weakness is uncertainty—no matter how much evidence we collect, our conclusions are never guaranteed.

You might think of the two forms of reasoning like this:

Deductive reasoning is like a blueprint—it works only if the materials are solid and the design is followed exactly.
Inductive reasoning is like weather forecasting—you make the best prediction based on past patterns, but the outcome can always change.

Both are necessary. In fact, most well-rounded arguments use a mix of both. For example, a persuasive speech on criminal justice reform might begin with statistical evidence (inductive), followed by an appeal to principles of fairness and equality under the law (deductive). A public health message might rely on broad scientific studies (inductive) and then use logic to propose specific actions (deductive).

Being able to switch between these two modes of reasoning is part of what makes a critical thinker adaptable. It allows you to understand where others are coming from, even when you don’t agree. It helps you question your own assumptions and weigh the strength of your conclusions. And it equips you to respond to complex problems with both logic and insight.

Applying the Concepts

To solidify your understanding, reflect on these questions or write a short journal response:

Think of a decision you made recently. Did you rely more on deductive or inductive reasoning?
Can you recall a time when someone used flawed reasoning to persuade you? What made the argument weak or strong?
Which type of reasoning feels more natural to you—and why?

Or, try this real-world application:

Scenario: A city is considering a ban on single-use plastic bags.

Inductive reasoning: What evidence might support the effectiveness of such a ban? What patterns have been observed in other cities?
Deductive reasoning: If the city’s values include environmental responsibility, and plastic pollution harms the environment, then should the city support the ban?

By practicing both forms of reasoning, you build not only sharper thinking skills—but also the confidence to use them in your writing, your conversations, and your choices.

Common Reasoning Mistakes in Inductive and Deductive Thinking

While deductive and inductive reasoning are powerful tools for arriving at conclusions, they are also vulnerable to specific types of errors. These mistakes often appear convincing on the surface but fall apart under scrutiny. By learning to recognize these patterns, we not only improve our ability to argue effectively—we also protect ourselves from being misled by others.

One common mistake in deductive reasoning is affirming the consequent. As we saw earlier, this fallacy assumes that if a result is observed, then the cause must be what we expected. For example:

If the store is open, the lights will be on.
The lights are on.
Therefore, the store is open.

This reasoning fails to consider other explanations—maybe someone is cleaning, or the lights were left on by mistake. In deductive logic, a conclusion only follows necessarily if the structure holds under all conditions.

Another deductive misstep is denying the antecedent. This happens when someone assumes that if one condition is false, the conclusion must also be false:

If I study, I’ll pass the test.
I didn’t study.
Therefore, I’ll fail the test.

This ignores the possibility that the person could pass by already knowing the material or by guessing correctly. Just because a premise is not met doesn’t guarantee the opposite conclusion.

On the inductive side, one of the most frequent fallacies is the hasty generalization. This occurs when someone draws a broad conclusion from too little evidence. For instance:

I met two rude people from that city.
Therefore, everyone from that city is rude.

This reasoning collapses under scrutiny because the sample size is far too small to support such a sweeping claim. Unfortunately, hasty generalizations are at the root of many stereotypes and prejudices.

Another error in inductive reasoning is the false cause fallacy, where someone assumes a causal relationship between two events simply because they occurred together. A famous example:

After the new mayor took office, crime went down.
Therefore, the new mayor reduced crime.

Correlation does not imply causation. The drop in crime could be due to seasonal changes, broader economic trends, or unrelated policing strategies. Critical thinkers are cautious about assigning cause without sufficient evidence.

There’s also the slippery slope fallacy, which assumes that one event will inevitably lead to a series of negative outcomes without providing evidence for that chain. For example:

If we allow students to redo one assignment, next they’ll expect to retake entire courses, and eventually no standards will remain.

This kind of argument uses fear rather than logic (Walton, 2008). It treats a minor concession as the first step toward collapse, even though the intermediate steps are never justified.

Each of these reasoning errors shares a common feature: they appear logical on the surface but fall apart when examined closely. They often succeed in persuasion precisely because they appeal to emotion, intuition, or bias. That’s why critical thinking requires more than intelligence—it requires vigilance and self-discipline. The best way to avoid these traps is to slow down, test your assumptions, and ask: Does the conclusion really follow from the premises? Have I considered alternative explanations? Is there enough evidence to justify this leap?

Recognizing these common reasoning flaws doesn’t just help us win debates—it helps us avoid making decisions we willl later regret. It trains us to listen more carefully, speak more thoughtfully, and reason more responsibly.

Conclusion: Choosing Reason Over Reaction

Deductive and inductive reasoning are not just academic tools—they are ways of making sense of the world. Whether we’re debating public policy, interpreting research, or deciding who to trust, our ability to reason clearly can shape the outcomes of our lives and communities. But clear thinking isn’t automatic. It requires practice, humility, and the courage to challenge what feels obvious.

This chapter has explored the structures, strengths, and pitfalls of both deductive and inductive reasoning. We’ve seen that deductive arguments can offer certainty—but only when their premises are true and their form is valid. Inductive arguments help us make predictions and learn from experience, but they require care in evaluating evidence and avoiding hasty conclusions. Both modes of reasoning are vulnerable to errors, especially when emotion, identity, or bias clouds our judgment.

As critical thinkers, our job is not to eliminate uncertainty but to manage it thoughtfully. When we slow down and analyze our thinking—when we ask, “Is this reasoning strong? Is this evidence enough?”—we sharpen our ability to engage ethically and effectively in the world. And in doing so, we don’t just make better arguments—we make better decisions.

Case Studies and Reflection Exercises

Applying critical thinking to real-world situations means stepping into the ambiguity and complexity of human decision-making. Rarely are arguments neatly labeled, and often, we must evaluate claims without complete information. The following case studies give you a chance to practice identifying reasoning types, recognizing fallacies, and reflecting on how context influences argument strength.

Case 1: The Apartment Complaint

Julian lives in a student apartment complex and is frustrated with the new manager. He’s noticed that the laundry machines have been broken for over two weeks and that several maintenance requests have gone unanswered. He tells his roommate, “This new manager doesn’t care about tenants. She’s already ignoring everyone, and I bet things will only get worse from here.”

Julian is using inductive reasoning based on his personal observations. His conclusion may feel justified, but how strong is his evidence? Could other factors be affecting the maintenance response? Is he generalizing from a small number of cases? This scenario invites reflection on sample size, bias, and the risk of forming hasty generalizations when emotions are involved.

Reflection Prompt: What additional information would strengthen or weaken Julian’s conclusion? How could he reframe his claim to avoid overgeneralization while still expressing a valid concern.

Case 2: The Political Campaign Ad

A political candidate runs a TV ad stating: “If we elect my opponent, crime will rise, jobs will vanish, and our community will suffer. Just look at what happened the last time their party was in charge.”

This argument uses emotionally charged language and an implied causal connection without offering direct evidence. The speaker implies that voting for the opponent will lead to disaster, invoking a slippery slope and possibly a false cause fallacy. The reasoning sounds persuasive, but it lacks concrete premises and leaps to a dramatic conclusion.

Reflection Prompt: What type of reasoning is used here—deductive, inductive, or neither? What logical fallacies are present? How could this message be tested or fact-checked?

Case 3: The COVID Misinformation Post

A social media post claims, “My cousin got the vaccine and still got COVID. So clearly the vaccine doesn’t work. Don’t believe what they’re telling you.”

This is a textbook example of weak inductive reasoning. The conclusion is based on a single anecdote, without context or scientific evidence. It also overlooks the difference between preventing infection and reducing severity. The reasoning appeals to personal experience, which is emotionally compelling but logically insufficient. It also may reflect confirmation bias, as the speaker appears to be seeking evidence to support a preexisting belief.

Reflection Prompt: Why do personal stories often feel more persuasive than statistics? How could this person improve their argument to make it more credible?

Case 4: The Classroom Policy

Professor Delgado tells her students, “If you turn in your paper late, you will lose 10%. This paper is late. Therefore, you will lose 10%.”

This is a clear example of deductive reasoning. It follows a valid form—modus ponens—where the conclusion logically follows from the rule. However, a student objects: “But I emailed you before the deadline and explained that I had a family emergency.” Now the professor must decide whether the rule applies absolutely or if exceptions are warranted. The reasoning is deductively sound, but context matters.

Reflection Prompt: What role do ethics, fairness, or policy exceptions play in deductive reasoning? Can a valid argument still be unfair?

These case studies highlight how reasoning works in lived experiences—not just on paper. To strengthen your critical thinking, try analyzing situations in your own life. Consider writing journal entries where you:

Identify an argument you’ve encountered recently (in conversation, media, or class).
Determine whether it was deductive or inductive.
Evaluate the argument’s strength, evidence, and fairness.
Reflect on how emotion, identity, or urgency may have shaped the reasoning.

By practicing this habit regularly, you will start to notice patterns and gain confidence in distinguishing sound arguments from weak ones—even in high-pressure or persuasive situations.

Practice Quiz: Deductive and Inductive Reasoning

Instructions: Identify whether the argument is primarily deductive, inductive, or flawed. Then briefly explain why.

Statement: All community colleges require placement tests. Berkeley City College is a community college. Therefore, Berkeley City College requires placement tests.
Your Answer: ___________
Why? ______________________________________

Statement: I’ve talked to three people who work at that coffee shop, and all of them were unfriendly. That place must have terrible customer service.
Your Answer: ___________
Why? ______________________________________

Statement: If it rains, we’ll cancel the event. It didn’t rain. Therefore, we didn’t cancel the event.
Your Answer: ___________
Why? ______________________________________

Statement: Crime increased after the new mayor took office. The mayor must be responsible for the crime wave.
Your Answer: ___________
Why? ______________________________________

Statement: In the past five years, our school’s debate team has placed in the top three every season. There’s a good chance we’ll do well again this year.
Your Answer: ___________
Why? ______________________________________

Extended Journal Prompts

Choose one of the following prompts and write a 300–500 word reflection:

1. Inductive Reasoning in Your Life
Think of a belief or assumption you’ve formed based on personal experience. How did you reach that conclusion? What patterns or observations did you rely on? Looking back, do you still feel the conclusion is strong, or do you see signs of hasty generalization?

2. When Logic Fell Short
Describe a time when someone used a deductively valid argument that still didn’t feel fair or persuasive. What made you resist the conclusion? Were the premises flawed? Did context or ethics play a role?

3. Reasoning in a Conflict
Reflect on a disagreement you’ve had—either personal, professional, or political. Did you or the other person rely more on inductive or deductive reasoning? How might better reasoning have improved the outcome?

4. Confirmation Bias in Action
Write about a time when you sought out information that supported what you already believed. Did you later discover counter-evidence? How did it affect your thinking?

5. Two Ways to Argue
Pick a topic you care about (e.g., climate change, school funding, free speech). Try to write both an inductive and a deductive argument supporting your position. Then evaluate: Which one is stronger—and why?

Glossary

Affirming the Consequent: A deductive fallacy where a conclusion is drawn based on the outcome being true, without confirming the cause. Example: “If it’s raining, the ground is wet. The ground is wet, so it must be raining.”

Argument: A set of statements in which one or more premises are offered to support a conclusion.

Causal Reasoning: A form of inductive reasoning that attempts to establish a cause-and-effect relationship between two events.

Claim: A statement or assertion presented as true, which an argument seeks to support with evidence.

Conclusion: The final statement in an argument that follows logically from the premises.

Conditional (if/then) statement

A logical statement that connects two ideas in the form “If A, then B.” The first part (the antecedent) sets a condition, and the second part (the consequent) states what follows if that condition is met. Conditional statements are central to deductive reasoning and are used in valid forms like modus ponens and modus tollens.

Counterexample

A specific case or example that disproves a general statement or weakens an argument. In deductive reasoning, a counterexample shows that the logic or form is invalid. In inductive reasoning, it challenges the strength or accuracy of a generalization by providing an exception.

Credibility: The perceived trustworthiness or reliability of a source or speaker.

Deductive Reasoning: Reasoning that starts with a general rule or principle and applies it to a specific case to draw a logically certain conclusion.

Denying the Antecedent: A deductive fallacy that assumes if the first part of a conditional statement is false, then the conclusion must also be false. Example: “If I study, I’ll pass. I didn’t study, so I’ll fail.”

Evidence: Facts, data, examples, or expert opinions used to support a claim or argument.

Experience: Personal knowledge gained through direct involvement. Useful in forming inductive reasoning, but limited in generalizability.

Fallacy: A flaw in reasoning that weakens an argument. Fallacies can be formal (structural) or informal (content-based).

False Cause Fallacy: A fallacy where one assumes a cause-and-effect relationship without sufficient evidence. Often confused with correlation.

False premise

A starting assumption in an argument that is factually incorrect or misleading. Even if the logic is valid, a false premise can make the entire argument unsound or unreliable. Identifying false premises is essential for evaluating whether a conclusion is truly justified.

Form: The logical structure of an argument. A valid form ensures that the conclusion follows logically from the premises.

Inductive generalization

A type of inductive reasoning where a conclusion about a whole group is drawn based on observations from a sample. The strength of an inductive generalization depends on the size, diversity, and representativeness of the sample. While often useful, generalizations can be misleading if based on too little or biased evidence.

Inductive Reasoning: Reasoning that draws general conclusions from specific observations. Inductive conclusions are probable, not certain.

Logic

The study and application of rules for valid reasoning. Logic helps us determine whether conclusions follow reliably from premises. It provides the structure that allows arguments to be tested for consistency, validity, and soundness—regardless of topic or content.

Modus Ponens: A valid deductive form: If A, then B. A is true. Therefore, B is true.

Modus Tollens: A valid deductive form: If A, then B. B is not true. Therefore, A is not true.

Overgeneralization: An inductive fallacy where a conclusion is drawn from too few or unrepresentative examples.

Premise: A statement in an argument that provides support or evidence for the conclusion.

Representativeness

The extent to which a sample or example accurately reflects the larger group or population it is intended to represent. In inductive reasoning, conclusions are stronger when based on representative evidence. A lack of representativeness can lead to biased generalizations and faulty conclusions.

Scientific reasoning

A method of thinking that uses observation, experimentation, and evidence to form and test hypotheses. Scientific reasoning relies on inductive patterns to identify trends and draw conclusions, while also using deductive logic to test predictions. It emphasizes skepticism, repeatability, and openness to revision based on new data.

Slippery Slope: A fallacy that argues one small step will inevitably lead to a chain of negative events without proving the connection.

Soundness: In deductive reasoning, an argument is sound if it is both valid (correct form) and has all true premises.

Validity: A property of deductive arguments where, if the premises are true, the conclusion must also be true due to correct logical form.

Weak Inductive Reasoning: Reasoning based on insufficient, biased, or unrepresentative evidence that makes the conclusion unreliable.

References

American Psychological Association. (2020). Publication manual of the American Psychological Association (7th ed.). https://doi.org/10.1037/0000165-000

Govier, T. (2018). A practical study of argument (8th ed.). Cengage Learning.

Kahneman, D. (2011). Thinking, fast and slow. Farrar, Straus and Giroux.

Moore, B. N., & Parker, R. (2017). Critical thinking (12th ed.). McGraw-Hill Education.

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

Walton, D. (2008). Informal logic: A pragmatic approach (2nd ed.). Cambridge University Press.

Weston, A. (2017). A rulebook for arguments (5th ed.). Hackett Publishing.