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Scientific Critical Thinking

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  • 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

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/k-dense-ai/scientific-critical-thinking/download

  2. 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

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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:

  1. Methodology critique — design, controls, confounding, and whether the method can answer the question asked.

  2. Bias detection — selection, measurement, publication, and cognitive biases.

  3. Statistical analysis evaluation — power, multiplicity, p-value misuse, effect sizes.

  4. Evidence quality assessment — study hierarchy, replication, and strength of inference.

  5. Logical fallacy identification — the fallacies that recur in scientific argument.

  6. Research design guidance — how to strengthen a design before data collection.

  7. 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

  1. 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

  2. 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

  3. 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

  4. 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

  5. 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:

  1. Summary: Brief overview of what was evaluated

  2. Strengths: What was done well (important for credibility and learning)

  3. 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)

  4. Specific Recommendations: Actionable suggestions for improvement

  5. 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:

  1. Identify strengths and weaknesses accurately

  2. Determine what conclusions are supported

  3. Recognize limitations and uncertainties

  4. Suggest improvements for future work

  5. 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

references/experimental_design.md

Experimental Design Checklist

Apply items to the actual design rather than treating checked boxes as a quality score. Framework versions and primary guidance: review_sources.md.

Research Question Formulation

Is the Question Well-Formed?

  • [ ] Specific: Clearly defined variables and relationships

  • [ ] Answerable: Can be addressed with available methods

  • [ ] Relevant: Addresses a gap in knowledge or practical need

  • [ ] Feasible: Resources, time, and ethical considerations allow it

  • [ ] Falsifiable: Can be proven wrong if incorrect

Have You Reviewed the Literature?

  • [ ] Identified what's already known

  • [ ] Found gaps or contradictions to address

  • [ ] Learned from methodological successes and failures

  • [ ] Identified appropriate outcome measures

  • [ ] Determined typical effect sizes in the field

Hypothesis Development

Is Your Hypothesis Testable?

  • [ ] Makes specific, quantifiable predictions

  • [ ] Variables are operationally defined

  • [ ] Specifies direction/nature of expected relationships

  • [ ] Can be falsified by potential observations

Types of Hypotheses

  • [ ] Null hypothesis (H₀): Explicit value or range, including any non-inferiority margin

  • [ ] Alternative hypothesis (H₁): Explicit competing value, range, or direction

  • [ ] Directional vs. non-directional: One-tailed vs. two-tailed tests

Study Design Selection

What Type of Study is Appropriate?

Experimental (Intervention) Studies:

  • [ ] Randomized Controlled Trial (RCT): Strong intervention-effect design when conduct and analysis preserve its identifying assumptions

  • [ ] Quasi-experimental: Non-random assignment but manipulation

  • [ ] Within-subjects: Same participants in all conditions

  • [ ] Between-subjects: Different participants per condition

  • [ ] Factorial: Multiple independent variables

  • [ ] Crossover: Participants receive multiple interventions sequentially

Observational Studies:

  • [ ] Cohort: Follow groups over time

  • [ ] Case-control: Compare those with/without outcome

  • [ ] Cross-sectional: Snapshot at one time point

  • [ ] Ecological: Population-level data

Consider:

  • [ ] Can you randomly assign participants?

  • [ ] Can you manipulate the independent variable?

  • [ ] Is the outcome rare (favor case-control) or common?

  • [ ] Do you need to establish temporal sequence?

  • [ ] What's feasible given ethical, practical constraints?

Variables

Independent Variables (Manipulated/Predictor)

  • [ ] Clearly defined and operationalized

  • [ ] Appropriate levels/categories chosen

  • [ ] Manipulation is sufficient to test hypothesis

  • [ ] Manipulation check planned (if applicable)

Dependent Variables (Outcome/Response)

  • [ ] Directly measures the construct of interest

  • [ ] Validated and reliable measurement

  • [ ] Sensitive enough to detect expected effects

  • [ ] Appropriate for statistical analysis planned

  • [ ] Primary outcome clearly designated

Control Variables

  • [ ] Confounding variables identified:

    • Variables that affect both IV and DV

    • Alternative explanations for findings

  • [ ] Strategy for control:

    • Randomization

    • Matching

    • Stratification

    • Statistical adjustment

    • Restriction (inclusion/exclusion criteria)

    • Avoid conditioning on mediators/colliders unless required by the target estimand

Extraneous Variables

  • [ ] Potential sources of noise identified

  • [ ] Standardized procedures to minimize

  • [ ] Environmental factors controlled

  • [ ] Time of day, setting, equipment standardized

Sampling

Population Definition

  • [ ] Target population: Who you want to generalize to

  • [ ] Accessible population: Who you can actually sample from

  • [ ] Sample: Who actually participates

  • [ ] Difference between these documented

Sampling Method

  • [ ] Probability sampling (preferred for generalizability):

    • Simple random sampling

    • Stratified sampling

    • Cluster sampling

    • Systematic sampling

  • [ ] Non-probability sampling (common but limits generalizability):

    • Convenience sampling

    • Purposive sampling

    • Snowball sampling

    • Quota sampling

Sample Size

  • [ ] Experimental/sampling unit and independent replicate count stated (animal, donor, litter, cage, site, culture, etc.); repeated measurements do not increase independent n

  • [ ] A priori power analysis conducted

    • Scientifically meaningful effect and plausible variability, with sensitivity analysis

    • Justified power or precision target (.80/.90 are conventions, not universal requirements)

    • Significance level (typically .05)

    • Statistical test to be used

  • [ ] Accounts for expected attrition/dropout

  • [ ] Accounts for clustering, repeated measures, and planned multiplicity

  • [ ] Sufficient for planned subgroup analyses

  • [ ] Practical constraints acknowledged

Inclusion/Exclusion Criteria

  • [ ] Clearly defined and justified

  • [ ] Not overly restrictive (limits generalizability)

  • [ ] Based on theoretical or practical considerations

  • [ ] Ethical considerations addressed

  • [ ] Documented and applied consistently

Blinding and Randomization

Randomization

  • [ ] What is randomized:

    • Participant assignment to conditions

    • Order of conditions (within-subjects)

    • Stimuli/items presented

  • [ ] Method of randomization:

    • Computer-generated random numbers

    • Random number tables

    • Coin flips (for very small studies)

  • [ ] Allocation concealment:

    • Sequence generated before recruitment

    • Allocation hidden until after enrollment

    • Secure centralized allocation, or sequentially numbered opaque sealed envelopes with safeguards preventing anticipation or tampering

  • [ ] Stratified randomization:

    • Balance important variables across groups

    • Block randomization to ensure equal group sizes

  • [ ] Check randomization:

    • Audit sequence generation and concealment; describe baseline characteristics

    • Do not use baseline significance tests as proof of successful/failed randomization

Blinding

  • [ ] Name who was blinded (participants, providers, assessors, analysts), to what, and how; single/double/triple-blind labels have inconsistent meanings

  • [ ] Blinding feasibility:

    • Is true blinding possible?

    • Placebo/sham controls needed?

    • Identical appearance of interventions?

  • [ ] Blinding check:

    • Document unblinding and its likely effect on this outcome

    • Assignment guesses after treatment can reflect efficacy/adverse effects; do not interpret them automatically as a test of blinding success

Control Groups and Conditions

What Type of Control?

  • [ ] No treatment control: Natural course of condition

  • [ ] Placebo control: Inert treatment for comparison

  • [ ] Active control: Standard treatment comparison

  • [ ] Wait-list control: Delayed treatment

  • [ ] Attention control: Matches contact time without active ingredient

Multiple Conditions

  • [ ] Factorial designs for multiple factors

  • [ ] Dose-response relationship assessment

  • [ ] Mechanism testing with component analyses

Procedures

Protocol Development

  • [ ] Detailed, written protocol:

    • Step-by-step procedures

    • Scripts for standardized instructions

    • Decision rules for handling issues

    • Data collection forms

  • [ ] Pilot tested before main study

  • [ ] Staff trained to criterion

  • [ ] Compliance monitoring planned

Standardization

  • [ ] Same instructions for all participants

  • [ ] Same equipment and materials

  • [ ] Same environment/setting when possible

  • [ ] Same assessment timing

  • [ ] Deviations from protocol documented

Data Collection

  • [ ] When collected:

    • Baseline measurements

    • Post-intervention

    • Follow-up timepoints

  • [ ] Who collects:

    • Trained researchers

    • Blinded when possible

    • Inter-rater reliability established

  • [ ] How collected:

    • Valid, reliable instruments

    • Standardized administration

    • Multiple methods if possible (triangulation)

Measurement

Validity

  • [ ] Face validity: Appears to measure construct

  • [ ] Content validity: Covers all aspects of construct

  • [ ] Criterion validity: Correlates with gold standard

    • Concurrent validity

    • Predictive validity

  • [ ] Construct validity: Measures theoretical construct

    • Convergent validity (correlates with related measures)

    • Discriminant validity (doesn't correlate with unrelated measures)

Reliability

  • [ ] Test-retest: Consistent over time

  • [ ] Internal consistency: Justify dimensionality and the chosen reliability model; Cronbach's α alone does not establish a single construct or validity

  • [ ] Inter-rater reliability: Agreement between raters (Cohen's κ, ICC)

  • [ ] Parallel forms: Alternative versions consistent

Measurement Considerations

  • [ ] Objective measures preferred when possible

  • [ ] Validated instruments used when available

  • [ ] Multiple measures of key constructs

  • [ ] Sensitivity to change considered

  • [ ] Floor/ceiling effects avoided

  • [ ] Response formats appropriate

  • [ ] Recall periods appropriate

  • [ ] Cultural appropriateness considered

Bias Minimization

Selection Bias

  • [ ] Random sampling when possible

  • [ ] Clearly defined eligibility criteria

  • [ ] Document who declines and why

  • [ ] Minimize self-selection

Performance Bias

  • [ ] Standardized protocols

  • [ ] Blinding of providers

  • [ ] Monitor protocol adherence

  • [ ] Document deviations

Detection Bias

  • [ ] Blinding of outcome assessors

  • [ ] Objective measures when possible

  • [ ] Standardized assessment procedures

  • [ ] Multiple raters with reliability checks

Attrition Bias

  • [ ] Strategies to minimize dropout

  • [ ] Track reasons for dropout

  • [ ] Compare dropouts to completers

  • [ ] Estimand and missing-outcome handling specified; an intention-to-treat label does not itself remove attrition bias

Reporting Bias

  • [ ] Preregister study and analysis plan

  • [ ] Designate primary vs. secondary outcomes

  • [ ] Commit to reporting all outcomes

  • [ ] Distinguish planned from exploratory analyses

Data Management

Data Collection

  • [ ] Data collection forms designed and tested

  • [ ] REDCap, Qualtrics, or similar platforms

  • [ ] Range checks and validation rules

  • [ ] Regular backups

  • [ ] Secure storage and documented privacy requirements for the institution/jurisdiction

Data Quality

  • [ ] Real-time data validation

  • [ ] Regular quality checks

  • [ ] Missing data patterns monitored

  • [ ] Outliers identified and investigated

  • [ ] Protocol deviations documented

Data Security

  • [ ] De-identification procedures

  • [ ] Access controls

  • [ ] Audit trails

  • [ ] Appropriate ethics-board review and applicable privacy-law requirements confirmed by the responsible institution; this checklist does not certify legal compliance

Statistical Analysis Planning

Analysis Plan (Prespecify Before Data Collection)

  • [ ] Primary analysis:

    • Statistical test(s) specified

    • Hypothesis clearly stated

    • Significance level set (usually α = .05)

    • One-tailed or two-tailed

  • [ ] Secondary analyses:

    • Clearly designated as secondary

    • Exploratory analyses labeled as such

  • [ ] Multiple comparisons:

    • Adjustment method specified (if needed)

    • Hypothesis family covers relevant outcomes, doses, time points, subgroups, and interim analyses; naming a primary outcome alone does not control all multiplicity

Assumptions

  • [ ] Assumptions of statistical tests identified

  • [ ] Plan to check assumptions

  • [ ] Prespecified robust or alternative analysis for the same estimand, with assumptions

  • [ ] Transformation options considered

Missing Data

  • [ ] Anticipated amount of missingness

  • [ ] Missing data mechanism (MCAR, MAR, MNAR)

  • [ ] Handling strategy:

    • Complete case analysis

    • Multiple imputation

    • Maximum likelihood

  • [ ] Sensitivity analyses planned

Effect Sizes

  • [ ] Appropriate effect size measures identified

  • [ ] Will be reported alongside p-values

  • [ ] Confidence intervals planned

Statistical Software

  • [ ] Software selected (R, SPSS, Stata, Python, etc.)

  • [ ] Version documented

  • [ ] Analysis scripts prepared in advance

  • [ ] Will be made available (Open Science)

Ethical Considerations

Ethical Approval

  • [ ] IRB/Ethics committee approval obtained

  • [ ] Study registered (ClinicalTrials.gov, etc.) if applicable

  • [ ] For medical research involving human participants, consult the current Declaration of Helsinki (2024 revision) and applicable local ethics requirements before the study

Informed Consent

  • [ ] Voluntary participation

  • [ ] Comprehensible explanation

  • [ ] Risks and benefits disclosed

  • [ ] Right to withdraw without penalty

  • [ ] Privacy protections explained

  • [ ] Compensation disclosed

Risk-Benefit Analysis

  • [ ] Potential benefits outweigh risks

  • [ ] Risks minimized

  • [ ] Vulnerable populations protected

  • [ ] Data safety monitoring (if high risk)

Confidentiality

  • [ ] Data de-identified

  • [ ] Secure storage

  • [ ] Limited access

  • [ ] Reporting doesn't allow re-identification

Validity Threats

Internal Validity (Causation)

  • [ ] History: External events between measurements

  • [ ] Maturation: Changes in participants over time

  • [ ] Testing: Effects of repeated measurement

  • [ ] Instrumentation: Changes in measurement over time

  • [ ] Regression to mean: Extreme scores becoming less extreme

  • [ ] Selection: Groups differ at baseline

  • [ ] Attrition: Differential dropout

  • [ ] Diffusion: Control group receives treatment elements

External Validity (Generalizability)

  • [ ] Sample representative of population

  • [ ] Setting realistic/natural

  • [ ] Treatment typical of real-world implementation

  • [ ] Outcome measures ecologically valid

  • [ ] Time frame appropriate

Construct Validity (Measurement)

  • [ ] Measures actually tap intended constructs

  • [ ] Operations match theoretical definitions

  • [ ] No confounding of constructs

  • [ ] Adequate coverage of construct

Statistical Conclusion Validity

  • [ ] Adequate statistical power

  • [ ] Assumptions met

  • [ ] Appropriate tests used

  • [ ] Alpha level appropriate

  • [ ] Multiple comparisons addressed

Reporting and Transparency

Preregistration

  • [ ] Study preregistered (OSF, ClinicalTrials.gov, AsPredicted)

  • [ ] Hypotheses stated a priori

  • [ ] Analysis plan documented

  • [ ] Distinguishes confirmatory from exploratory

Reporting Guidelines

  • [ ] RCT results: CONSORT 2025 checklist (with applicable design/intervention extensions)

  • [ ] Randomized trial protocols: SPIRIT 2025

  • [ ] Observational studies: STROBE checklist

  • [ ] Systematic reviews: PRISMA 2020 checklist

  • [ ] Diagnostic accuracy studies: STARD 2015 and applicable extensions (including STARD-AI for AI diagnostic accuracy studies)

  • [ ] Qualitative interviews/focus groups: COREQ checklist

  • [ ] Case reports: CARE guidelines

Reporting checklists improve transparency; they are not risk-of-bias tools or certificates of valid conduct. Select extensions for the design instead of assuming every older extension has been revised to the new core checklist.

Transparency

  • [ ] All measures reported

  • [ ] All manipulations disclosed

  • [ ] Sample size determination explained

  • [ ] Exclusion criteria and numbers reported

  • [ ] Attrition documented

  • [ ] Deviations from protocol noted

  • [ ] Conflicts of interest disclosed

Open Science

  • [ ] Data sharing planned (when ethical)

  • [ ] Analysis code shared

  • [ ] Materials available

  • [ ] Preprint posted

  • [ ] Open access publication when possible

Post-Study Considerations

Data Analysis

  • [ ] Follow preregistered plan

  • [ ] Clearly label deviations and exploratory analyses

  • [ ] Check assumptions

  • [ ] Report all outcomes

  • [ ] Report effect sizes and CIs, not just p-values

Interpretation

  • [ ] Conclusions supported by data

  • [ ] Limitations acknowledged

  • [ ] Alternative explanations considered

  • [ ] Generalizability discussed

  • [ ] Clinical/practical significance addressed

Dissemination

  • [ ] Publish regardless of results (reduce publication bias)

  • [ ] Present at conferences

  • [ ] Share findings with participants (when appropriate)

  • [ ] Communicate to relevant stakeholders

  • [ ] Plain language summaries

Next Steps

  • [ ] Replication needed?

  • [ ] Follow-up studies identified

  • [ ] Mechanism studies planned

  • [ ] Clinical applications considered

Common Pitfalls to Avoid

  • [ ] No justified sample-size/precision rationale; absence of a power calculation alone does not prove that the completed study was underpowered

  • [ ] Hypothesis formed after seeing data (HARKing)

  • [ ] Lack of blinding with a plausible route to biased care, measurement, or analysis

  • [ ] P-hacking (data fishing, optional stopping)

  • [ ] Multiple testing without correction → false positives

  • [ ] Inadequate control group

  • [ ] Confounding not addressed

  • [ ] Instruments not validated

  • [ ] High attrition not addressed

  • [ ] Cherry-picking results to report

  • [ ] Causal language from correlational data

  • [ ] Ignoring assumptions of statistical tests

  • [ ] Not preregistering changes literature bias

  • [ ] Conflicts of interest not disclosed

Final Checklist Before Starting

  • [ ] Research question is clear and important

  • [ ] Hypothesis is testable and specific

  • [ ] Study design is appropriate

  • [ ] Sample size is adequate (power analysis)

  • [ ] Measures are valid and reliable

  • [ ] Confounds are controlled

  • [ ] Randomization and blinding implemented

  • [ ] Data collection is standardized

  • [ ] Analysis plan is prespecified

  • [ ] Ethical approval obtained

  • [ ] Study is preregistered

  • [ ] Resources are sufficient

  • [ ] Team is trained

  • [ ] Protocol is documented

  • [ ] Backup plans exist for problems

Remember

Good experimental design is about:

  • Asking clear questions

  • Minimizing bias

  • Maximizing validity

  • Appropriate inference

  • Transparency

  • Reproducibility

The best time to think about these issues is before collecting data, not after.

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.

Security

No issues found by our static scan· No issues found

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by
K-Dense Inc.
Publisher
K-Dense (Community)
Source
K-Dense-AI/scientific-agent-skills · skills/scientific-critical-thinking · commit a25cf9e
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE.md)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

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Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download scientific-critical-thinking.zip