scientific-method
Research methodology for hypothesis testing, evidence evaluation, and verification standards in geoscience research. Use when designing experiments, formulating hypotheses, or evaluating evidence quality.
When & Why to Use This Skill
This Claude skill provides a rigorous framework for applying the scientific method within geoscience research. It streamlines the process of hypothesis testing, evidence evaluation, and experimental design, ensuring that findings meet high verification standards and minimize cognitive bias through structured validation hierarchies.
Use Cases
- Formulating falsifiable and specific hypotheses for geological studies using standardized templates.
- Evaluating the strength of research claims by applying a tiered evidence hierarchy to multi-proxy datasets.
- Designing validation experiments with clearly defined positive and negative controls to ensure reproducibility.
- Refining academic manuscripts by implementing precise uncertainty language and identifying potential 'red flags' like confirmation bias.
- Conducting breakthrough skepticism checks to independently verify calculations and explore alternative explanations for major discoveries.
| name | scientific-method |
|---|---|
| description | Research methodology for hypothesis testing, evidence evaluation, and verification standards in geoscience research. Use when designing experiments, formulating hypotheses, or evaluating evidence quality. |
| location | user |
Scientific Method for Geoscience Research
When to Use This Skill
Invoke when:
- Formulating or testing hypotheses
- Designing validation experiments
- Evaluating strength of evidence
- Deciding if a finding is confirmed vs preliminary
- Setting up controls and reproducibility checks
Core Principles
1. Evidence Hierarchy
Apply this hierarchy when evaluating claims:
| Level | Description | Example |
|---|---|---|
| Tier 1 | Multi-proxy validation | δ18O + Mg/Ca + δ13C all show signal |
| Tier 2 | Two independent lines | δ18O + historical documentation |
| Tier 3 | Single proxy | Only δ18O shows anomaly |
2. Verification Standards
Minimum for confident claims:
- One source = coincidence (interesting but unverified)
- Two sources = clue (worth investigating)
- Three sources = verified (minimum for publication)
3. Hypothesis Formulation
Good hypotheses are:
- Falsifiable: Define what evidence would disprove it
- Specific: Include testable predictions with measurable outcomes
- Bounded: State assumptions and limitations upfront
Template:
HYPOTHESIS: [Claim]
PREDICTION: If true, we should observe [specific outcome]
FALSIFICATION: If we observe [contrary evidence], hypothesis is rejected
ASSUMPTIONS: [List key assumptions]
4. Controls and Reproducibility
For each analysis, identify:
- Positive controls: Known events that SHOULD be detected
- Negative controls: Periods that SHOULD NOT show signal
- Blind tests: Analyze data without knowing expected result first
5. Uncertainty Language
Use precise language:
| Term | Meaning | When to Use |
|---|---|---|
| "proposed" | Unconfirmed hypothesis | Single line of evidence |
| "likely" | Probable (2+ sources) | Two independent confirmations |
| "confirmed" | Multi-proxy validated | Three+ independent lines |
| "suggests" | Indicates direction | Preliminary interpretation |
| "consistent with" | Doesn't contradict | Supportive but not proof |
NEVER use: "100%", "definitely", "certainly", "must be", "proves"
6. Red Flags
Stop and reconsider if:
- Finding seems too clean/perfect
- No alternative explanations considered
- Post-hoc selection of "successful" cases
- Confirmation bias (looking for expected result)
- Circular reasoning (using conclusion as premise)
7. Breakthrough Skepticism
When you think you've made a major discovery:
- Pause - Don't immediately declare success
- Check arithmetic - Verify calculations independently
- Seek alternatives - What else could explain this?
- Consult literature - Has this been tried before?
- Sleep on it - Fresh eyes often find flaws
Workflow for New Hypothesis
1. STATE the hypothesis clearly
2. DEFINE falsification criteria
3. IDENTIFY positive/negative controls
4. GATHER data (ideally blind to expected result)
5. ANALYZE using pre-defined methods
6. EVALUATE against falsification criteria
7. DOCUMENT regardless of outcome
8. REPORT uncertainty honestly
Example: Testing Earthquake Detection
Hypothesis: Speleothem δ18O detects M6+ earthquakes within 50 km
Prediction: Known historical earthquakes should show z > 2.0 anomalies within ±10 years of event
Falsification: If >50% of known earthquakes show no anomaly, hypothesis is rejected
Positive controls: 1896 Independence M6.3 (48 km from Crystal Cave)
Negative controls: Periods with no documented seismicity
Test result: 6/6 positive controls detected → Tier 2 evidence (small n, needs replication)