Scientific Critical Thinking

K-DenseCommunityCreated by K-Dense Inc.

Skill
Research and analysis
Science and research
MIT
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From the author

Evaluates scientific claims and evidence quality. Applies to experimental design validity, biases and confounders, statistical interpretation, evidence grading frameworks (GRADE, Cochrane Risk of Bias), and teaching critical analysis. Supports evidence appraisal and identifying flaws; formal peer review writing belongs to peer-review.

— the description by K-Dense Inc.

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

Scientific Critical Thinking provides a structured approach to assessing the validity of research claims. It guides users through the appraisal of experimental designs, statistical interpretations, and potential biases using established frameworks like GRADE and Cochrane Risk of Bias. The skill emphasizes constructive, evidence-based critique, helping users distinguish between data and interpretation while maintaining transparency regarding uncertainty.

What it does

Evaluates research methodology and experimental design validity.
Identifies cognitive, methodological, and statistical biases.
Applies formal evidence grading frameworks like GRADE and Cochrane RoB.
Provides structured guidance for writing traceable, constructive critiques.
Assists in separating supported conclusions from assumptions and recommendations.
Offers optional visual aids for bias and quality assessment workflows.

When to use it

When reviewing scientific papers or research protocols.
When conducting systematic reviews or meta-analyses.
When assessing the strength of evidence for a specific scientific claim.
When identifying potential flaws in experimental design or statistical analysis.
When teaching critical analysis of scientific literature.

Advantages

Promotes consistent, objective evaluation standards.
Reduces reliance on intuition by using systematic checklists.
Encourages constructive feedback that improves research quality.
Provides clear, actionable steps for identifying and reporting study limitations.
Supports rigorous, evidence-based decision making.

Usage examples

“Review this study's methodology for potential selection bias and confounding.”: The agent will analyze the study design, check for baseline imbalances, evaluate the randomization process, and identify potential threats to internal validity.
“Apply the GRADE framework to the evidence presented in this paper.”: The agent will assess the certainty of the evidence across domains like risk of bias, inconsistency, and imprecision, providing a structured quality rating.
“Create a flowchart illustrating the risk of bias assessment process for a randomized trial.”: The agent will generate a visual diagram outlining the decision steps for evaluating bias in a randomized controlled trial.
“Critique the statistical analysis used in this clinical trial report.”: The agent will examine the power, multiplicity, and model assumptions, highlighting any misuse of p-values or inappropriate effect size interpretations.

Install

Download the skill as a ZIP:
https://autogineer.com/agent-skills/k-dense-ai/scientific-critical-thinking/download

Unzip it into the cross-client skills folder of your project:

mkdir -p .agents/skills && unzip scientific-critical-thinking.zip -d .agents/skills

Documentation by K-Dense Inc.

Scientific Critical Thinking

Overview

Critical thinking is a systematic process for evaluating scientific rigor. Assess methodology, experimental design, statistical validity, biases, confounding, and evidence quality using GRADE and Cochrane ROB frameworks. Apply this skill for critical analysis of scientific claims.

When to Use This Skill

This skill should be used when:

Evaluating research methodology and experimental design
Assessing statistical validity and evidence quality
Identifying biases and confounding in studies
Reviewing scientific claims and conclusions
Conducting systematic reviews or meta-analyses
Applying GRADE or Cochrane risk of bias assessments
Providing critical analysis of research papers

Appraisal Workflow

Fix the question and unit. Record population, intervention/exposure, comparator, outcome, time point, effect measure, and target setting. Distinguish descriptive, predictive, and causal claims. Identify the independent experimental/sampling unit.
Extract evidence before judging. Locate the numerical result, uncertainty, denominators, protocol/registration, analysis plan, and relevant supplementary material. Keep missing reporting separate from evidence that a procedure was not performed.
Assess design and analysis. Check selection, confounding, measurement, attrition, multiplicity, dependence, and model assumptions. Good fit or optimizer convergence does not establish a uniquely identified parameter or a causal effect.
Choose the right appraisal framework. Reporting completeness, risk of bias, and certainty of a body of evidence answer different questions. Record the exact tool version and the result being assessed; do not turn checklist counts into a quality score.
Synthesize with scope intact. Examine independent replication, overlapping samples, missing evidence, and applicability. Apply GRADE per outcome/comparison when appropriate, with explicit domain reasons, rather than grading a whole paper by its design label.
Write a traceable critique. For each concern give the source location, observation, consequence for the claim, uncertainty, and a feasible remedy. Separate supported conclusions from assumptions and from clinical/policy recommendations.

Current framework versions, primary sources, and verification limits are in references/review_sources.md. The examples in the references are teaching examples, not empirical findings or validated patient-specific advice.

Visual Aids (Optional)

Only add figures when the user explicitly requests a diagram (for example, a GRADE flowchart, bias decision tree, or evidence-quality framework).

When figures help:

Critical thinking framework diagrams
Bias identification decision trees
Evidence quality assessment flowcharts
GRADE or risk-of-bias evaluation frameworks

How to create figures:

Preferred: Use the scientific-schematics skill for AI-generated diagrams from a natural-language description
Alternative: Build figures in your usual tools (draw.io, PowerPoint, matplotlib, etc.)

Run from the repository root, with OPENROUTER_API_KEY set:

python skills/scientific-schematics/scripts/generate_schematic.py "Illustrative GRADE appraisal: define outcome and comparison, assess certainty domains with reasons; keep recommendation decisions separate" -o figures/grade_flowchart.png --doc-type report

This optional command's CLI was checked with --help; paid generation was not exercised for this example. Follow that skill's current dependencies and review every generated label.

Disclosure: AI schematic generation sends your prompt to OpenRouter (a third-party API). Do not include unpublished sensitive details unless that transmission is appropriate for your project.

Core Capabilities

Seven capability areas, each with the questions to ask and what the answers imply, are in references/core_capabilities.md:

Methodology critique — design, controls, confounding, and whether the method can answer the question asked.
Bias detection — selection, measurement, publication, and cognitive biases.
Statistical analysis evaluation — power, multiplicity, p-value misuse, effect sizes.
Evidence quality assessment — study hierarchy, replication, and strength of inference.
Logical fallacy identification — the fallacies that recur in scientific argument.
Research design guidance — how to strengthen a design before data collection.
Claim evaluation — separating what was shown from what is being asserted.

Per-topic detail is in references/scientific_method.md, references/common_biases.md, references/statistical_pitfalls.md, references/evidence_hierarchy.md, references/logical_fallacies.md, and references/experimental_design.md.

Application Guidelines

General Approach

Be Constructive
- Identify strengths as well as weaknesses
- Suggest improvements rather than just criticizing
- Distinguish between fatal flaws and minor limitations
- Recognize that all research has limitations
Be Specific
- Point to specific instances (e.g., "Table 2 shows..." or "In the Methods section...")
- Quote problematic statements
- Provide concrete examples of issues
- Reference specific principles or standards violated
Be Proportionate
- Match criticism severity to issue importance
- Distinguish between major threats to validity and minor concerns
- Consider whether issues affect primary conclusions
- Acknowledge uncertainty in your own assessments
Apply Consistent Standards
- Use same criteria across all studies
- Don't apply stricter standards to findings you dislike
- Acknowledge your own potential biases
- Base judgments on methodology, not results
Consider Context
- Acknowledge practical and ethical constraints
- Consider field-specific norms for effect sizes and methods
- Recognize exploratory vs. confirmatory contexts
- Account for resource limitations in evaluating studies

Apply risk-of-bias tools to the right unit

For RoB 2, identify the specific result: outcome, time point, intervention comparison, numerical estimate, and effect of assignment versus adherence. Use the variant for individually randomized, cluster, or crossover trials; record signalling answers and justifications rather than assigning one blanket score to the whole paper. Different outcomes in the same trial can have different bias judgments. See the Cochrane RoB 2 guidance.

For non-randomized intervention effects, state whether using ROBINS-I 2016 or the ROBINS-I V2 November 2025 draft for follow-up/cohort studies. Do not mix their domains or algorithms. Diagnostic accuracy appraisal now uses QUADAS-3 (current tool v1.2), at the accuracy-estimate level. See the tool-specific sources before a formal assessment.

When Providing Critique

Structure feedback as:

Summary: Brief overview of what was evaluated
Strengths: What was done well (important for credibility and learning)
Concerns: Issues organized by severity
- Critical issues (threaten validity of main conclusions)
- Important issues (affect interpretation but not fatally)
- Minor issues (worth noting but don't change conclusions)
Specific Recommendations: Actionable suggestions for improvement
Overall Assessment: Balanced conclusion about evidence quality and what can be concluded

Use precise terminology:

Name specific biases, fallacies, and methodological issues
Reference established standards and guidelines
Cite principles from scientific methodology
Use technical terms accurately

When Uncertain

Acknowledge uncertainty: "This could be X or Y; additional information needed is Z"
Ask clarifying questions: "Was [methodological detail] done? This affects interpretation."
Provide conditional assessments: "If X was done, then Y follows; if not, then Z is concern"
Note what additional information would resolve uncertainty

Reference Materials

This skill includes comprehensive reference materials that provide detailed frameworks for critical evaluation:

references/scientific_method.md - Core principles of scientific methodology, the scientific process, critical evaluation criteria, red flags in scientific claims, causal inference standards, peer review, and open science principles
references/common_biases.md - Comprehensive taxonomy of cognitive, experimental, methodological, statistical, and analysis biases with detection and mitigation strategies
references/statistical_pitfalls.md - Common statistical errors and misinterpretations including p-value misunderstandings, multiple comparisons problems, sample size issues, effect size mistakes, correlation/causation confusion, regression pitfalls, and meta-analysis issues
references/evidence_hierarchy.md - Traditional evidence hierarchy, GRADE system, study quality assessment criteria, domain-specific considerations, evidence synthesis principles, and practical decision frameworks
references/logical_fallacies.md - Logical fallacies common in scientific discourse organized by type (causation, generalization, authority, relevance, structure, statistical) with examples and detection strategies
references/experimental_design.md - Comprehensive experimental design checklist covering research questions, hypotheses, study design selection, variables, sampling, blinding, randomization, control groups, procedures, measurement, bias minimization, data management, statistical planning, ethical considerations, validity threats, and reporting standards

When to consult references:

Load references into context when detailed frameworks are needed
Search references for specific topics with your available text-search tool.
References provide depth; SKILL.md provides procedural guidance
Consult references for comprehensive lists, detailed criteria, and specific examples

Remember

Scientific critical thinking is about:

Systematic evaluation using established principles
Constructive critique that improves science
Proportional confidence to evidence strength
Transparency about uncertainty and limitations
Consistent application of standards
Recognition that all research has limitations
Balance between skepticism and openness to evidence

Always distinguish between:

Data (what was observed) and interpretation (what it means)
Correlation and causation
Statistical significance and practical importance
Exploratory and confirmatory findings
What is known and what is uncertain
Evidence against a claim and evidence for the null

Goals of critical thinking:

Identify strengths and weaknesses accurately
Determine what conclusions are supported
Recognize limitations and uncertainties
Suggest improvements for future work
Advance scientific understanding

Citing Scientific Agent Skills

This skill is part of Scientific Agent Skills by K-Dense. If it materially contributed to a manuscript, report, presentation, or code release, add the paper to the references or software section and tell the user you did so:

Kassis, T., Agarwal, V., He, Y., Patel, D., & Brueckner, A. M. (2026). Scientific Agent Skills: A Library of Procedural Knowledge for Research Agents. arXiv:2609.00065. https://doi.org/10.48550/arXiv.2609.00065

Always cite the current version. The DOI and https://arxiv.org/abs/2609.00065 resolve to the latest arXiv version, so never append a version suffix such as v1. When network access is available, fetch https://arxiv.org/abs/2609.00065 (or http://export.arxiv.org/api/query?id_list=2609.00065) before writing the reference and take the author list, year, and version from that record. If the record lists a journal reference or publisher DOI, cite the published version instead.

Files

ATTRIBUTION.mdAdded by AutoGineer1.0 KiB
LICENSELicence file added by AutoGineer1.0 KiB
SKILL.md12.8 KiB
references/common_biases.md12.1 KiB
references/core_capabilities.md16.6 KiB
references/evidence_hierarchy.md17.0 KiB
references/experimental_design.md17.8 KiB
references/logical_fallacies.md18.2 KiB
references/review_sources.md7.4 KiB
references/scientific_method.md7.5 KiB
references/statistical_pitfalls.md20.7 KiB

references/logical_fallacies.md

Logical Fallacies in Scientific Discourse

The examples are illustrative arguments, not clinical claims or evidence about a named study. Critique the inferential step; a fallacious argument does not make its conclusion false. Methodological sources are in review_sources.md.

Fallacies of Causation

1. Post Hoc Ergo Propter Hoc (After This, Therefore Because of This)

Description: Assuming that because B happened after A, A caused B.

Examples:

"I took this supplement and my cold went away, so the supplement cured my cold."
"Autism diagnoses increased after vaccine schedules changed, so vaccines cause autism."
"I wore my lucky socks and won the game, so the socks caused the win."

Why fallacious: Temporal sequence is necessary but not sufficient for causation. Correlation ≠ causation.

Related: Cum hoc ergo propter hoc (with this, therefore because of this) - correlation mistaken for causation even without temporal order.

2. Confusing Correlation with Causation

Description: Assuming correlation implies direct causal relationship.

Examples:

"Countries that eat more chocolate have more Nobel Prize winners, so chocolate makes you smarter."
"Ice cream sales correlate with drowning deaths, so ice cream causes drowning."

Reality: Often due to confounding variables (hot weather causes both ice cream sales and swimming).

3. Reverse Causation

Description: Confusing cause and effect direction.

Examples:

"Depression is associated with inflammation, so inflammation causes depression." (Could be: depression causes inflammation)
"Wealthy people are healthier, so wealth causes health." (Could be: health enables wealth accumulation)

Solution: Establish temporal order and assess confounding, selection, and measurement; longitudinal observation alone does not identify a causal direction.

4. Single Cause Fallacy

Description: Attributing complex phenomena to one cause when multiple factors contribute.

Examples:

"Crime is caused by poverty." (Ignores many other contributing factors)
"Heart disease is caused by fat intake." (Oversimplifies multifactorial disease)

Reality: Most outcomes have multiple contributing causes.

Fallacies of Generalization

5. Hasty Generalization

Description: Drawing broad conclusions from insufficient evidence.

Examples:

"My uncle smoked and lived to 90, so smoking isn't dangerous."
"This drug worked in 5 patients, so it's effective for everyone."
"I saw three black swans, so all swans are black."

Why fallacious: Small, unrepresentative samples don't support universal claims.

6. Anecdotal Fallacy

Description: Using personal experience or isolated examples as proof.

Examples:

"I know someone who survived cancer using alternative medicine, so it works."
"My grandmother never exercised and lived to 100, so exercise is unnecessary."

Why fallacious: Anecdotes are observations, but selection, memory, and missing comparators usually prevent the claimed general or causal inference. Do not dismiss a safety signal merely because it begins with a case report.

7. Cherry Picking (Suppressing Evidence)

Description: Selecting only evidence that supports your position while ignoring contradictory evidence.

Examples:

Citing only studies showing supplement benefits while ignoring null findings
Highlighting successful predictions while ignoring failed ones
Showing graphs that start at convenient points

Detection: Look for systematic reviews, not individual studies.

8. Ecological Fallacy

Description: Inferring individual characteristics from group statistics.

Example:

"Average income in this neighborhood is high, so this person must be wealthy."
"This country is richer and has less disease, so its richer individuals must have less disease."

Why fallacious: Group-level patterns don't necessarily apply to individuals.

Fallacies of Authority and Tradition

9. Appeal to Authority (Argumentum ad Verecundiam)

Description: Accepting claims because an authority figure said them, without evidence.

Examples:

"Dr. X says this treatment works, so it must." (If Dr. X provides no data)
"Einstein believed in God, so God exists." (Einstein's physics expertise doesn't transfer)
"99% of doctors recommend..." (Appeal to majority + authority without evidence)

Valid use of authority: Experts providing evidence-based consensus in their domain.

Invalid: Authority opinions without evidence, or outside their expertise.

10. Appeal to Antiquity/Tradition

Description: Assuming something is true or good because it's old or traditional.

Examples:

"Traditional medicine has been used for thousands of years, so it must work."
"This theory has been accepted for decades, so it must be correct."

Why fallacious: Age doesn't determine validity. Many old beliefs have been disproven.

11. Appeal to Novelty

Description: Assuming something is better because it's new.

Examples:

"This is the latest treatment, so it must be superior."
"New research overturns everything we knew." (Often overstated)

Why fallacious: New ≠ better. Established treatments often outperform novel ones.

Fallacies of Relevance

12. Ad Hominem (Attack the Person)

Description: Attacking the person making the argument rather than the argument itself.

Types:

Abusive: "He's an idiot, so his theory is wrong."
Circumstantial: "She's funded by industry, so her findings are false."
Tu Quoque: "You smoke, so your anti-smoking argument is invalid."

Why fallacious: Personal characteristics don't determine argument validity.

Note: Conflicts of interest are worth noting but don't invalidate evidence.

13. Genetic Fallacy

Description: Judging something based on its origin rather than its merits.

Examples:

"This idea came from a drug company, so it's wrong."
"Ancient Greeks believed this, so it's outdated."

Better approach: Evaluate evidence regardless of source.

14. Appeal to Emotion

Description: Manipulating emotions instead of presenting evidence.

Types:

Appeal to fear: "If you don't vaccinate, your child will die."
Appeal to pity: "Think of the suffering patients who need this unproven treatment."
Appeal to flattery: "Smart people like you know that..."

Why fallacious: Emotional reactions don't determine truth.

15. Appeal to Consequences (Argumentum ad Consequentiam)

Description: Arguing something is true/false based on whether consequences are desirable.

Examples:

"Climate change can't be real because the solutions would hurt the economy."
"Free will must exist because without it, morality is impossible."

Why fallacious: Reality is independent of what we wish were true.

16. Appeal to Nature

Description: Assuming "natural" means good, safe, or effective.

Examples:

"This treatment is natural, so it's safe."
"Organic food is natural, so it's healthier."
"Vaccines are unnatural, so they're harmful."

Why fallacious:

Many natural things are deadly (arsenic, snake venom, hurricanes)
Many synthetic things are beneficial (antibiotics, vaccines)
"Natural" is often poorly defined

17. Moralistic Fallacy

Description: Assuming what ought to be true is true.

Examples:

"There shouldn't be sex differences in ability, so they don't exist."
"People should be rational, so they are."

Why fallacious: Desires about reality don't change reality.

Fallacies of Structure

18. False Dichotomy (False Dilemma)

Description: Presenting only two options when more exist.

Examples:

"Either you're with us or against us."
"It's either genetic or environmental." (Usually both)
"Either the treatment works or it doesn't." (Ignores partial effects)

Reality: Most issues have multiple options and shades of gray.

19. Begging the Question (Circular Reasoning)

Description: Assuming what you're trying to prove.

Examples:

"This medicine works because it has healing properties." (What are healing properties? That it works!)
"God exists because the Bible says so, and the Bible is true because it's God's word."

Detection: Check if the conclusion is hidden in the premises.

20. Moving the Goalposts

Description: Changing standards of evidence after initial standards are met.

Example:

Skeptic: "Show me one study."
[Shows study]
Skeptic: "That's just one study; show me a meta-analysis."
[Shows meta-analysis]
Skeptic: "But meta-analyses have limitations..."

Why problematic: No amount of evidence will ever be sufficient.

21. Slippery Slope

Description: Arguing that one step will inevitably lead to extreme outcomes without justification.

Example:

"If we allow gene editing for disease, we'll end up with designer babies and eugenics."

When valid: If intermediate steps are actually likely.

When fallacious: If chain of events is speculative without evidence.

22. Straw Man

Description: Misrepresenting an argument to make it easier to attack.

Example:

Position: "We should teach evolution in schools."
Straw man: "So you think we should tell kids they're just monkeys?"

Detection: Ask: Is this really what they're claiming?

Fallacies of Statistical and Scientific Reasoning

23. Texas Sharpshooter Fallacy

Description: Cherry-picking data clusters to fit a pattern, like shooting arrows then drawing targets around them.

Examples:

Finding cancer clusters and claiming environmental causes (without accounting for random clustering)
Data mining until finding significant correlations

Why fallacious: Patterns in random data are inevitable; finding them doesn't prove causation.

24. Base Rate Fallacy

Description: Ignoring prior probability when evaluating evidence.

Example:

Disease prevalence is 0.1%; test sensitivity and specificity are both 99%
Positive test ≠ 99% probability of disease
Positive predictive value is 0.99*0.001 / (0.99*0.001 + 0.01*0.999) = 0.0902 (9.02%), assuming these parameters apply to the tested population. Overall accuracy alone would not determine this probability.

Solution: Use Bayesian reasoning; consider base rates.

25. Prosecutor's Fallacy

Description: Confusing P(Evidence|Innocent) with P(Innocent|Evidence).

Example:

"The probability of this DNA match occurring by chance is 1 in 1 million, so there's only a 1 in 1 million chance the defendant is innocent."

Why fallacious: Ignores base rates and prior probability.

26. McNamara Fallacy (Quantitative Fallacy)

Description: Focusing only on what can be easily measured while ignoring important unmeasured factors.

Example:

Judging school quality only by test scores (ignoring creativity, social skills, ethics)
Measuring healthcare only by quantifiable outcomes (ignoring quality of life)

Quote: "Not everything that counts can be counted, and not everything that can be counted counts."

27. Multiple Comparisons Fallacy

Description: Not accounting for increased false positive rate when testing many hypotheses.

Example:

Twenty independent true-null tests at alpha .05 give 1 - 0.95**20 = 0.6415 probability of at least one false rejection; correlated tests need a different calculation
Claiming jellybean color X causes acne after testing 20 colors

Solution: Correct for multiple comparisons (Bonferroni, FDR).

28. Reification (Hypostatization)

Description: Treating abstract concepts as if they were concrete things.

Examples:

"Evolution wants organisms to survive." (Evolution doesn't "want")
"The gene for intelligence" (Intelligence isn't one gene)
"Nature selects..." (Nature doesn't consciously select)

Why problematic: Can lead to confused thinking about mechanisms.

Fallacies of Scope and Definition

29. No True Scotsman

Description: Retroactively excluding counterexamples by redefining criteria.

Example:

"No natural remedy has side effects."
"But poison ivy is natural and causes reactions."
"Well, no true natural remedy has side effects."

Why fallacious: Moves goalposts to protect claim from falsification.

30. Equivocation

Description: Using a word with multiple meanings inconsistently.

Example:

"Evolution is just a theory. Theories are guesses. So evolution is just a guess."
(Conflates colloquial "theory" with scientific "theory")

Detection: Check if key terms are used consistently.

31. Ambiguity

Description: Using vague language that can be interpreted multiple ways.

Example:

"Quantum healing" (What does "quantum" mean here?)
"Natural" (Animals? Not synthetic? Organic? Common?)

Why problematic: Claims become unfalsifiable when terms are undefined.

32. Mind Projection Fallacy

Description: Projecting mental constructs onto reality.

Example:

Assuming categories that exist in language exist in nature
"Which chromosome is the gene for X on?" when X is polygenic and partially environmental

Better: Recognize human categories may not carve nature at the joints.

Fallacies Specific to Science

33. Galileo Gambit

Description: "They laughed at Galileo, and he was right, so if they're laughing at me, I must be right too."

Why fallacious:

They laughed at Galileo, and he was right
They also laughed at countless crackpots who were wrong
Being an outsider doesn't make you right

Reality: Revolutionary ideas are usually well-supported by evidence.

34. Argument from Ignorance (Ad Ignorantiam)

Description: Assuming something is true because it hasn't been proven false (or vice versa).

Examples:

"No one has proven homeopathy doesn't work, so it works."
"We haven't found evidence of harm, so it must be safe."

Why fallacious: Absence of evidence ≠ evidence of absence (though it can be, depending on how hard we've looked).

Burden of proof: Falls on the claimant, not the skeptic.

35. God of the Gaps

Description: Explaining gaps in knowledge by invoking supernatural or unfalsifiable causes.

Examples:

"We don't fully understand consciousness, so it must be spiritual."
"This complexity couldn't arise naturally, so it must be designed."

Why problematic:

Fills gaps with non-explanations
Discourages genuine investigation
History shows gaps get filled by natural explanations

36. Nirvana Fallacy (Perfect Solution Fallacy)

Description: Rejecting solutions because they're imperfect.

Examples:

"Vaccines aren't 100% effective, so they're worthless."
"This diet doesn't work for everyone, so it doesn't work."

Reality: Most interventions are partial; perfection is rare.

Better: Compare to alternatives, not to perfection.

37. Special Pleading

Description: Applying standards to others but not to oneself.

Examples:

"My anecdotes count as evidence, but yours don't."
"Mainstream medicine needs RCTs, but my alternative doesn't."
"Correlation doesn't imply causation—except when it supports my view."

Why fallacious: Evidence standards should apply consistently.

38. Unfalsifiability

Description: Formulating claims in ways that cannot be tested or disproven.

Examples:

"This energy can't be detected by any instrument."
"It works, but only if you truly believe."
"Failures prove the conspiracy is even deeper."

Why problematic: Unfalsifiable claims aren't scientific; they can't be tested.

Good science: Makes specific, testable predictions.

39. Affirming the Consequent

Description: If A, then B. B is true. Therefore, A is true.

Example:

"If the drug works, symptoms improve. Symptoms improved. Therefore, the drug worked."
(Could be placebo, natural history, regression to mean)

Why fallacious: Other causes could produce the same outcome.

Valid form: Modus ponens: If A, then B. A is true. Therefore, B is true.

40. Denying the Antecedent

Description: If A, then B. A is false. Therefore, B is false.

Example:

"If you have fever, you have infection. You don't have fever. Therefore, you don't have infection."

Why fallacious: B can be true even when A is false.

Avoiding Logical Fallacies

Practical Steps

Identify the claim - What exactly is being argued?
Identify the evidence - What supports the claim?
Check the logic - Does the evidence actually support the claim?
Look for hidden assumptions - What unstated beliefs does the argument rely on?
Consider alternatives - What other explanations fit the evidence?
Check for emotional manipulation - Is the argument relying on feelings rather than facts?
Evaluate the source - Are there conflicts of interest? Is this within their expertise?
Look for balance - Are counterarguments addressed fairly?
Assess the evidence - Is it anecdotal, observational, or experimental? How strong?
Be charitable - Interpret arguments in their strongest form (steel man, not straw man).

Questions to Ask

Is the conclusion supported by the premises?
Are there unstated assumptions?
Is the evidence relevant to the conclusion?
Are counterarguments acknowledged?
Could alternative explanations account for the evidence?
Is the reasoning consistent?
Are terms defined clearly?
Is evidence being cherry-picked?
Are emotions being manipulated?
Would this reasoning apply consistently to other cases?

Common Patterns

Good Arguments:

Clearly defined terms
Relevant, sufficient evidence
Valid logical structure
Acknowledges limitations and alternatives
Proportional conclusions
Transparent about uncertainty
Applies consistent standards

Poor Arguments:

Vague or shifting definitions
Irrelevant or insufficient evidence
Logical leaps
Ignores counterevidence
Overclaimed conclusions
False certainty
Double standards

Remember

Fallacious reasoning doesn't mean the conclusion is false - just that this argument doesn't support it.
Identifying fallacies isn't about winning - it's about better understanding reality.
We all commit fallacies - recognizing them in ourselves is as important as in others.
Charity principle - Interpret arguments generously; don't assume bad faith.
Focus on claims, not people - Ad hominem goes both ways.

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Declares allowed tools, Needs network access, Needs an API key, Needs Python

The author says: Analytical guidance needs no network. Optional figures via the scientific-schematics skill require OPENROUTER_API_KEY and outbound API access to OpenRouter.