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