LSAT Logic Applied

A new press release from Gatorade claims that over 150 million Americans feel dehydrated—and positions its products as a science-backed solution that can hydrate “better, faster, or longer than water.” That sounds compelling. But what exactly is being claimed?In this episode of LSAT Logic Applied, I break down the argument using core LSAT concepts like equivocation, necessary assumptions, problem-solution framing, and vague appeals to authority. When does “feeling dehydrated” actually mean dehydration? What does “better than water” mean without context? And is hydration really confusing—or just being framed that way?This episode explores how definitions, framing, and selective use of science can turn an ordinary condition into a large-scale problem with a convenient solution. If you’re interested in applying LSAT-style reasoning to marketing, health claims, and everyday arguments, this one’s for you.

A new large-scale study found that some medications used by patients with irritable bowel syndrome—especially antidepressants, and in IBS-D patients loperamide and diphenoxylate—were associated with higher all-cause mortality, while other IBS treatments were not. That is a serious finding. But what exactly does it prove?In this episode of LSAT Logic Applied, I break down the study using classic LSAT concepts like causation versus correlation, confounding by indication, scope, statistical framing, and overreading cautious conclusions. The central lesson is simple but important: a strong association can justify concern without yet proving that the medication itself caused the outcome.This is a good example of how scientific headlines can be both grounded and easy to overread. When does “linked to” become “caused by?” What would strengthen that inference, and what would weaken it? And how should we think about large observational studies that are careful, sophisticated, and still not the same thing as proof?If you like applying LSAT-style reasoning to medicine, research, and public discourse, this episode is for you.

A Washington Post story reports that more Americans are millionaires than ever before—but many of them still do not feel rich. That sounds intuitive in an economy shaped by inflation, soaring housing costs, and retirement wealth that exists mostly on paper rather than in cash. But what exactly follows from that?In this episode of LSAT Logic Applied, I break down the argument using classic LSAT concepts like equivocation, alternative explanations, statistical framing, and scope shift. The key question is not whether $1 million means less than it once did. It does. The harder question is whether that means millionaire status no longer signals wealth—or whether the argument quietly changes the meaning of “rich” along the way.This is a great case study in how real-world arguments often hinge not on disputed facts, but on slippery categories. When does “not as rich as before” become “not rich at all”? And how do we tell the difference between feeling middle-class and actually being middle-class?If you like applying LSAT-style reasoning to economics, policy, and public discourse, this episode is for you.

The U.S. Postal Service says it may run out of cash within a year—and is suspending pension contributions to free up $2.5 billion. The logic seems straightforward: there’s a problem, this action helps solve it, so it must be justified.But does that conclusion actually follow?In this episode of LSAT Logic Applied, we break down the argument using core LSAT concepts like necessary vs. sufficient conditions, alternative explanations, and scope shifts. While the USPS identifies a real financial constraint and a measure that alleviates it, the reasoning may go a step too far—treating one workable solution as if it were the only viable option.Along the way, we explore key logical questions:When does a solution become a necessity?How do we spot ignored alternatives in policy arguments?And what assumptions are required to justify a major financial decision?This isn’t just about the Postal Service—it’s about how easily “this helps” turns into “this must be done.”If you want to sharpen your ability to analyze real-world arguments with LSAT precision, this episode is a perfect case study.

A new study of CCTV cameras in Newark suggests something surprising: crime reduction effects may take years to appear. In the short term, the cameras showed little impact—but after three to four years, auto theft dropped significantly. Researchers call these “sleeper effects,” and the policy takeaway is intuitive: short-term studies may miss what really matters.But does that conclusion follow?In this episode of LSAT Logic Applied, we break down the argument using two classic LSAT lenses: Parallel Reasoning and Parallel Flaw. First, we examine the study’s strongest move—testing outcomes across different time horizons to capture effects that unfold slowly. Then we look at where the reasoning may go too far, shifting from “longer studies can reveal effects” to “short-term studies are unreliable.”Along the way, we explore key logical issues:When does better evidence justify broader conclusions?How do we distinguish methodological insight from overgeneralization?And what assumptions are required to treat time as the decisive factor?This isn’t just about surveillance or crime policy. It’s about how we interpret evolving evidence—and how easily strong reasoning can slide into flawed conclusions.If you want to sharpen your ability to analyze real-world arguments the way the LSAT does, this episode is a perfect case study.

A new study reports a striking result: a lab-engineered diet boosted bee colony growth by up to 15 times. The implication? A potential breakthrough in protecting pollinators—and even stabilizing global food supply chains.But how strong is that argument, really?In this episode, we apply LSAT Logical Reasoning tools to unpack the claim. We examine the difference between causation and real-world impact, the risks of generalizing from controlled experiments, and the subtle shift from a narrow scientific result to sweeping policy implications.The core question isn’t whether the science is impressive—it is.The question is whether the conclusion goes further than the evidence can support.If a solution works in the lab, does that mean it solves the problem in the wild?And when does a compelling mechanism become an overstated claim?This episode breaks it down.

Can activity on the Sun trigger earthquakes on Earth? A new scientific model suggests that solar storms may influence fault lines through changes in the ionosphere, potentially nudging already unstable regions of the Earth’s crust toward seismic activity.It’s a striking idea—one that combines space weather, geophysics, and a plausible physical mechanism. But does the evidence actually support the conclusion?In this episode of LSAT Logic Applied, we break down the argument using core LSAT reasoning tools. We examine the difference between correlation and causation, explore the risk of reverse causality, and ask whether a theoretical mechanism is enough to establish real-world impact. We also look at how selective evidence and unfalsifiable framing can make a claim sound stronger than it is.This episode isn’t about whether solar activity affects the atmosphere—it’s about whether that influence meaningfully translates into earthquakes. And more broadly, it’s about how to evaluate scientific claims that connect complex systems with limited evidence.If you want to sharpen your ability to spot weak assumptions and overextended conclusions, this is exactly the kind of argument the LSAT is designed to test.

Philadelphia officials have proposed a $1 rideshare tax to help close a $300 million school budget deficit—but does raising some revenue actually solve the problem?In this episode of LSAT Logic Applied, we break down the argument behind the policy using classic LSAT reasoning tools. The proposal is straightforward: schools need funding, the tax generates about $48 million per year, and that revenue could help prevent layoffs. But on closer inspection, the logic depends on several unstated assumptions.We explore a core LSAT flaw—confusing a partial solution with a sufficient one—along with hidden assumptions about consumer behavior, who actually bears the cost of the tax, and whether the policy addresses the underlying causes of the deficit. We also examine how projections can break down if real-world behavior changes.This episode isn’t about whether schools deserve funding. It’s about how policy arguments are constructed—and how they can overstate what a proposal actually accomplishes.If you want to sharpen your ability to evaluate real-world claims using clean, structured reasoning, this is exactly the kind of argument the LSAT is designed to test.

A new study claims that most people misunderstand the environmental impact of the food they eat—and that better labeling could fix the problem. But does the evidence actually support that conclusion?In this episode, we break down the logic behind that claim using LSAT-style analysis. We look at the study’s design, including its small and localized sample, the use of controlled sorting tasks instead of real-world behavior, and the assumption that scientific models of environmental impact provide a definitive benchmark.From there, we explore deeper reasoning issues: the difference between heuristics and errors, the gap between stated intentions and actual behavior, and the risks of overgeneralizing from limited data.The result is a classic LSAT problem hiding in plain sight: when a study shows that people’s beliefs don’t perfectly match the data, does that mean they’re wrong—or just simplifying a complex system in a practical way?If you want to get better at spotting flawed reasoning in research, policy, and everyday claims, this episode is a perfect place to start.

Sea-level rise is one of the most closely watched indicators of climate change—and one of the most heavily modeled. But recent claims suggest that some of those projections may be flawed because earlier models relied on incomplete assumptions about the Earth’s gravity and shape, known as geoid models.In this episode of LSAT Logic Applied, Andrew Leahey examines the reasoning behind that claim. If earlier models had methodological flaws, does that necessarily mean current sea-level projections are wrong? Or does that argument rely on hidden assumptions about how scientific models evolve over time?Using LSAT logical reasoning tools, this episode explores common flaws involving methodology critiques, overgeneralization, and the gap between identifying a problem and proving a conclusion. Along the way, it highlights a key analytical question: when does a flaw in a model actually undermine the broader claim—and when does it not?The goal isn’t to resolve the science of sea-level rise. Instead, it’s to better understand how arguments about scientific uncertainty are constructed, and how careful reasoning can clarify what the evidence truly supports.Whether you’re interested in climate science or just want to sharpen your analytical skills, this episode breaks down a real-world claim using the same logic tested on the LSAT.

Artificial intelligence is quickly becoming part of everyday health decisions. Millions of people now consult AI tools like ChatGPT for explanations of symptoms, medical information, and possible diagnoses. But recent studies have raised concerns that chatbots may sometimes give misleading or even dangerous medical advice.In this episode of LSAT Logic Applied, Andrew Leahey examines the reasoning behind those warnings. Studies have found that AI systems can misidentify conditions or recommend the wrong next steps in simulated medical scenarios—but does that necessarily mean the technology is unreliable in real-world situations?Using classic LSAT logical reasoning tools, this episode explores a key question: when researchers test AI in controlled experiments, how confidently can we generalize those results to everyday use? Along the way, we examine a common reasoning flaw involving experimental conditions, alternative explanations, and the difference between identifying a potential risk and proving a broader conclusion.The goal isn’t to decide whether people should use AI for health advice. Instead, the focus is on how arguments about new technologies are constructed—and how careful logical analysis can reveal what the evidence actually supports.Even if you’ve never taken the LSAT, the reasoning skills used in this episode apply to debates about technology, medicine, and scientific claims more broadly.

A common pain reliever has become the center of a heated debate. Some commentators have claimed do 1that taking Tylenol during pregnancy may contribute to autism in children, pointing to studies that show a statistical association between prenatal acetaminophen use and later autism diagnoses. But does that association actually prove causation?In this episode of LSAT Logic Applied, Andrew Leahey breaks down the reasoning behind that claim using the same analytical tools tested on the LSAT. Observational studies can reveal patterns in data—but patterns alone don’t establish cause and effect. When two things occur together, there may be other explanations: underlying illness, confounding variables, or broader health factors that influence both medication use and developmental outcomes.Using this real-world example, the episode explores one of the most common reasoning flaws tested on the LSAT: mistaking correlation for causation. The question isn’t whether the research is worth studying—it’s whether the argument built on that research actually supports the conclusion being drawn.If you want to sharpen your ability to spot weak reasoning in science, media, and public debate, this episode shows how LSAT logic can help separate evidence from inference.

Scientists studying academic fraud recently issued a striking warning: fraudulent scientific papers may now be appearing at a faster rate than legitimate ones. The claim comes from new research examining so-called “paper mills”—organized networks that mass-produce fake academic papers and sell authorship positions to researchers looking to inflate their publication records.At first glance, the conclusion sounds alarming. If fake research is spreading faster than real science, the integrity of scientific literature could be at risk. But what does “faster” actually mean?In this episode of LSAT Logic Applied, Andrew Leahey breaks down the argument using a classic LSAT reasoning lens. The key issue turns out to be a subtle but important distinction between growth rates and total quantities. Something can grow faster while still remaining a tiny fraction of the whole.By unpacking the logic behind the headline, this episode shows how growth statistics can create dramatic conclusions—and how the LSAT mindset helps reveal what the argument actually proves.

A recent court fight over tariffs has produced a fascinating real-world example of a classic LSAT logical flaw.In this episode of LSAT Logic Applied, Andrew Leahey breaks down an argument made by U.S. Customs and Border Protection in federal court. The agency warned that refunding certain tariffs could take years, pointing to a similar government refund program from the 1990s that also took years to complete. At first glance, that reasoning sounds sensible: if a massive refund program took years before, why wouldn’t another one take years now?But when we apply LSAT logic, the argument reveals a familiar issue: a weak analogy.Analogical reasoning depends on the idea that two situations are comparable in the ways that matter. Yet the earlier refund program took place decades ago, under very different technological and administrative conditions. Today’s trade enforcement systems involve digital databases, automated entry systems, and entirely different reporting structures. Without evidence that the two situations share the same operational constraints, the analogy may not support the conclusion as strongly as it appears.This episode walks through the structure of the argument and shows how the LSAT trains you to dissect claims like this. Along the way, you’ll learn how to identify weak analogies, why they’re common in legal and policy debates, and how small gaps in reasoning can shape big conclusions.You don’t need to be studying for the LSAT to follow along. The goal is simple: learn how to apply clean logical thinking to messy real-world arguments.

A recent study of more than 130,000 adults found that people prescribed melatonin long-term had significantly higher rates of heart failure and mortality. Headlines quickly followed, raising concerns about the safety of one of the most widely used sleep supplements.But what does the study actually show?In this episode of LSAT Logic Applied, Andrew Leahey uses the tools of LSAT logical reasoning to examine the claim. We break down common analytical pitfalls including selection bias, contaminated control groups, reverse causation, relative versus absolute risk, and hidden confounding variables.The goal isn’t to give medical advice—it’s to show how the reasoning behind high-profile health claims can be evaluated using the same logic principles tested on the LSAT.If you’ve ever wondered how correlation studies can mislead—even when the numbers sound alarming—this episode walks through the argument step by step.Because good reasoning isn’t just for test day.

An internal FDA memo claims that COVID-19 vaccines caused at least ten child deaths and suggests sweeping regulatory reform may be necessary. In this episode, I’m not debating vaccine policy—I’m applying LSAT Logical Reasoning tools to the structure of the argument itself.We examine selective skepticism, necessary assumptions, burden of proof, and scope shifts. When does incomplete data invalidate a conclusion—and when is it used to support one? If flawed denominators undermine infection studies, can passive reporting systems simultaneously justify strong causal claims?This is a clean logic breakdown of a high-stakes argument. No politics. Just structure.

We all know the common LSAT Logical Reasoning question types: necessary assumptions, flaws, strengthen, weaken, causation, scope shifts.But here’s the better question:Why should anyone care about those forms of inquiry outside a standardized test?In this episode of LSAT Logic Applied, Andrew Leahey steps back from breaking down a single news story and instead explains why the LSAT’s core question types mirror the structure of real-world argument.Every tax proposal rests on hidden assumptions.Every policy debate turns on causation claims.Every sweeping reform risks a scope shift.Every confident headline can be weakened—or strengthened—by new evidence.The LSAT doesn’t just test logic under time pressure. It trains intellectual discipline:• What must be true for this claim to hold?• Where does the reasoning break?• What evidence would actually change my mind?• Does the conclusion match the strength of the evidence?In a world saturated with confident assertions and thin premises, those questions matter.This episode isn’t about beating the test.It’s about learning how arguments actually work—and how they fall apart.Subscribe for more short episodes applying clean logic to messy real-world claims.

In this episode of LSAT Logic Applied, we break down the EPA’s decision to rescind the 2009 Greenhouse Gas Endangerment Finding and eliminate federal vehicle emissions standards.The legal fight isn’t just about climate policy — it’s about logic.Under the Clean Air Act, the EPA must regulate vehicle emissions if it determines they “cause or contribute” to air pollution that endangers public health. In 2009, the agency made that finding. Now, it argues that even if greenhouse gases contribute to climate change, that alone does not authorize regulation in the way prior administrations claimed.That’s a classic LSAT issue: necessary vs. sufficient conditions.Was the Endangerment Finding a trigger that required regulation? Or merely a prerequisite that still needed additional congressional authorization?We walk through:How conditional logic works in statutesThe difference between necessary and sufficient conditionsHow courts and agencies insert hidden premisesAnd why this kind of reasoning shows up constantly in real-world disputesYou don’t need to be studying for the LSAT to follow along. This episode is about spotting logical shifts — the kind the LSAT punishes and the real world rewards.

Can a president argue that civil litigation is too distracting to defend—while filing multiple civil lawsuits of his own?In this episode of LSAT Logic Applied, we break down a Reuters story involving Donald Trump and his claim that a sitting president should receive temporary immunity from civil lawsuits because litigation burdens the presidency. At the same time, he initiated several civil suits in his personal capacity.Using classic LSAT Logical Reasoning tools, we analyze two structural flaws: behavioral inconsistency and scope shift. Does the principle “litigation burdens the presidency” apply symmetrically? Or does the argument quietly move from protecting the office to protecting the officeholder?We also situate the issue alongside the Supreme Court’s 1997 civil immunity decision involving Bill Clinton and examine how precedent interacts with argument structure.This episode is a clean demonstration of how LSAT reasoning tools—assumptions, symmetry, and flaw analysis—translate directly into real-world legal disputes.If you want to think more clearly about how arguments work (and when they don’t), this one’s for you.

In this episode of LSAT Logic Applied, Andrew Leahey breaks down one of the most tested—and most misunderstood—skills on the LSAT: inference questions. Using a recent scientific claim about prehistoric humans shaping European ecosystems, Andrew walks through how Inference to the Best Explanation (IBE) works both on the exam and in real-world research reporting.You’ll learn:The difference between Must Be True and Most Strongly Supported questionsWhy “better explanatory fit” does not automatically equal proven causationHow LSAT inference questions punish strength creep and outside knowledgeThe hidden assumption that connects model improvement to real-world causationHow to spot when an argument quietly shifts from data to explanationThis episode is especially useful if you’ve ever struggled with inference questions—or if you’ve noticed how often headlines slide from “associated with” to “therefore caused by.”You don’t need to be studying for the LSAT to benefit. This is about sharpening your reasoning in a world full of confident conclusions.If you enjoy the show, subscribe and share. New episodes apply clean LSAT logic to messy real-world claims.

Does cannabis protect the aging brain—or are we watching correlation quietly turn into causation?In this episode of LSAT Logic Applied, Andrew Leahey breaks down a newly accepted study from the Journal of Studies on Alcohol and Drugs titled “Lifetime Cannabis Use Is Associated with Brain Volume and Cognitive Function in Middle-Aged and Older Adults.” Using UK Biobank data, the study finds that lifetime cannabis use is associated with larger brain volumes in certain regions and better cognitive performance later in life.But association is not explanation.Applying LSAT Necessary Assumption analysis, this episode shows how scientific findings—carefully written and caveated—can be misread once assumptions about causation, directionality, and confounding factors sneak in. Learn how to spot the unstated premise holding the argument together, and why negating it causes the explanation to fall apart.You don’t need to be studying for the LSAT to follow along—this is about learning how real-world arguments work, and where they quietly overreach.Subscribe for more short episodes applying clean logic to messy claims.

Zaxby’s says fans are craving the return of its Southern Fried Shrimp and Giant Quesadillas—so bringing them back is a no-brainer. But what, exactly, does that claim depend on?In this episode of LSAT Logic Applied, we use a real corporate press release from Zaxbys to break down a classic LSAT skill: identifying a necessary assumption—the unstated idea an argument must rely on to work at all.No politics, no policy. Just clean logic, marketing language, and the hidden reasoning that holds the argument together.If you’ve ever wondered how confident claims get their persuasive force, this episode shows you where to look.

We break down a real-world opinion piece on immigration enforcement to illustrate one of the LSAT’s most fundamental skills: identifying the main conclusion of an argument.Using a recent news column calling for the removal of a senior White House official, the episode shows how facts, narratives, and moral language can obscure the actual claim an author is trying to prove. By stripping the argument down LSAT-style, Andrew demonstrates how to separate premises from conclusions—and why confusing the two is one of the most common Logical Reasoning mistakes.

Today’s episode is about necessary assumptions — the unstated ideas an argument must rely on to reach its conclusion, even if no one ever says them out loud.

FCC Chair Brendan Carr recently said there are “legitimate competition concerns” about Netflix’s proposed $83 billion acquisition of Warner Bros. studios and HBO Max.Here’s the twist: the FCC has no authority to review or approve the deal.The FCC regulates broadcast licenses, which aren’t part of this transaction. But Carr made public comments anyway, raising red flags about consolidation in the streaming market. And his comments were picked up as if they had regulatory teeth.So what’s the logical structure here — and where does it go wrong?

The story comes from a BBC report about a mushroom called Lanmaoa asiatica — native to parts of China and the Philippines — that causes remarkably consistent hallucinations of “lilliputian figures”: tiny humans marching on tabletops, climbing up walls, even clinging to your soup spoon.These hallucinations only seem to happen when the mushroom is undercooked, and they’ve been reported across different cultures and time periods. So far, no one’s been able to identify the active compound, and researchers are still trying to explain why the visions are so consistent — both in content and duration.Some scientists suggest that the mushroom contains a unique hallucinogen. Others point to historical accounts of “mushroom madness” in Papua New Guinea that may involve the same species. And the assumption lurking underneath much of the coverage is this:The mushroom causes people to hallucinate tiny people.

The claim comes from Donald Trump, who has said, more than once, that tariffs made the United States the richest nation in the world.FactCheck.org pushed back on that — pointing to economic data and broader historical context that challenges the accuracy of the statement.But for LSAT purposes, we don’t need to resolve the economics. We’re not here to litigate macroeconomic history. We’re here to look at the logic of the claim — and that’s where things get interesting.