| name | science |
| description | Scientific analysis and data reasoning. Use for "analyze", "scientific method", "hypothesis", and "research". |
| trigger | /oma:science |
Skill: science
Apply scientific method to analyze data and test hypotheses.
When to Use
- Data analysis and interpretation
- Forming and testing hypotheses
- Experimental design
- Statistical reasoning
- Pattern discovery
Scientific Process
1. Observe
- What data do we have?
- What patterns exist?
- What anomalies appear?
2. Question
- Why does this pattern exist?
- What could explain this?
- What are the alternatives?
3. Hypothesize
- Form testable explanations
- Identify predictions
- Consider null hypothesis
4. Test
- Design experiments
- Collect data
- Run analysis
5. Analyze
- Statistical tests
- Effect sizes
- Confidence intervals
- Significance
6. Conclude
- Accept/reject hypothesis
- Limitations acknowledged
- Future work identified
Analysis Techniques
Descriptive Statistics
- Mean, median, mode
- Standard deviation
- Range and quartiles
- Distribution shape
Correlation Analysis
- Pearson correlation
- Spearman correlation
- Effect sizes
- Confounders
Hypothesis Testing
- T-tests
- Chi-square
- ANOVA
- Non-parametric tests
Regression
- Linear regression
- Logistic regression
- Multivariate analysis
Data Quality
Check for:
- Missing data
- Outliers
- Errors
- Biases
- Confounds
Address with:
- Imputation
- Robust methods
- Sensitivity analysis
- Subgroup analysis
Output Format
## Science: {question}
### Observation
{data summary}
### Hypothesis
**H1:** {primary hypothesis}
**H0:** {null hypothesis}
### Analysis
**Method:** {statistical approach}
**Assumptions:** {list}
### Results
| Measure | Value | 95% CI |
|---------|-------|--------|
| {stat} | {val} | {ci} |
**Effect size:** {value}
**p-value:** {value}
### Interpretation
{what this means}
### Limitations
{caveats and concerns}
### Conclusion
{decision}
Constraints
- Show your work
- Report uncertainty
- Use appropriate methods
- Consider alternatives
- Be honest about limitations