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Scientific Critical Thinking

K-DenseCommunityCreated by K-Dense Inc.

  • Skill
  • Research and analysis
  • Science and research
  • MIT
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From the author

Evaluate scientific claims and evidence quality. Use for assessing experimental design validity, identifying biases and confounders, applying evidence grading frameworks (GRADE, Cochrane Risk of Bias), or teaching critical analysis. Best for understanding evidence quality, identifying flaws. For formal peer review writing use 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 enables users to perform rigorous critiques of methodology, statistical analysis, and experimental design. By leveraging established frameworks like GRADE and Cochrane Risk of Bias, the skill helps identify biases, logical fallacies, and confounding factors, ensuring that conclusions are supported by the evidence presented. It is designed to foster constructive, evidence-based analysis while maintaining transparency regarding uncertainty and limitations.

What it does

  • Critiques research methodology and experimental design for validity and rigor.
  • Identifies cognitive, selection, and measurement biases in scientific studies.
  • Evaluates statistical validity, including power, effect sizes, and p-value usage.
  • Applies formal evidence grading frameworks like GRADE and Cochrane ROB.
  • Detects logical fallacies and separates data from interpretation in scientific claims.
  • Provides actionable recommendations to strengthen research designs before data collection.

When to use it

  • When evaluating the methodology and conclusions of a research paper.
  • When designing a new experiment and seeking to minimize potential biases.
  • When conducting a systematic review or meta-analysis of existing literature.
  • When assessing the strength of evidence supporting a specific scientific claim.
  • When teaching or learning critical analysis techniques in a scientific context.

Advantages

  • Promotes consistent, objective evaluation standards across different studies.
  • Encourages constructive critique that identifies both strengths and weaknesses.
  • Provides a comprehensive taxonomy of biases and logical fallacies for easy reference.
  • Helps distinguish between major validity threats and minor limitations.
  • Supports transparent reporting of uncertainty and evidence strength.

Usage examples

“Evaluate the experimental design of this study for potential selection bias and confounding variables.”
The agent will analyze the study's participant selection process, identify potential systematic differences between groups, and assess whether the study design adequately controls for identified confounders.
“Apply the GRADE framework to assess the quality of evidence in this meta-analysis.”
The agent will evaluate the study based on risk of bias, inconsistency, indirectness, imprecision, and publication bias to determine the overall certainty of the evidence.
“Identify potential logical fallacies in the discussion section of this paper.”
The agent will scan the text for common errors in reasoning, such as correlation-causation confusion or overgeneralization, and explain why these arguments are logically flawed.
“Review my proposed study protocol and suggest improvements to strengthen internal validity.”
The agent will review the protocol, suggest specific adjustments to randomization, blinding, or control groups, and highlight potential threats to validity that should be addressed.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/k-dense-ai/scientific-critical-thinking-udjd/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

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 "GRADE evidence assessment flowchart with downgrade and upgrade factors" -o figures/grade_flowchart.png --doc-type report

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

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

  • Use grep to search references for specific topics: grep -r "pattern" references/

  • 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

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): No effect/relationship exists

  • [ ] Alternative hypothesis (H₁): Effect/relationship exists

  • [ ] 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): Gold standard for causation

  • [ ] 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)

    • Blinding

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

  • [ ] A priori power analysis conducted

    • Expected effect size (from literature or pilot)

    • Desired power (typically .80 or .90)

    • Significance level (typically .05)

    • Statistical test to be used

  • [ ] Accounts for expected attrition/dropout

  • [ ] 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

    • Sequentially numbered, sealed envelopes (if needed)

  • [ ] Stratified randomization:

    • Balance important variables across groups

    • Block randomization to ensure equal group sizes

  • [ ] Check randomization:

    • Compare groups at baseline

    • Report any significant differences

Blinding

  • [ ] Single-blind: Participants don't know group assignment

  • [ ] Double-blind: Participants and researchers don't know

  • [ ] Triple-blind: Participants, researchers, and data analysts don't know

  • [ ] Blinding feasibility:

    • Is true blinding possible?

    • Placebo/sham controls needed?

    • Identical appearance of interventions?

  • [ ] Blinding check:

    • Assess whether blinding maintained

    • Ask participants/researchers to guess assignments

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: Items measure same construct (Cronbach's α)

  • [ ] 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

  • [ ] Intention-to-treat analysis planned

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 (HIPAA/GDPR compliant if needed)

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

  • [ ] Compliance with regulations (IRB, HIPAA, GDPR)

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)

    • Primary outcome protects from inflation

Assumptions

  • [ ] Assumptions of statistical tests identified

  • [ ] Plan to check assumptions

  • [ ] Backup non-parametric alternatives

  • [ ] 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

  • [ ] Protocol follows Declaration of Helsinki or equivalent

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

  • [ ] RCTs: CONSORT 2010 checklist (with applicable extensions)

  • [ ] Observational studies: STROBE checklist

  • [ ] Systematic reviews: PRISMA 2020 checklist

  • [ ] Diagnostic studies: STARD checklist

  • [ ] Qualitative research: COREQ checklist

  • [ ] Case reports: CARE guidelines

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 power analysis → underpowered study

  • [ ] Hypothesis formed after seeing data (HARKing)

  • [ ] No blinding when feasible → bias

  • [ ] 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/claude-scientific-writer · skills/scientific-critical-thinking · commit 529b9f7
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced
Also distributed in
K-Dense-AI/claude-scientific-writer

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