Statistical Analysis Prompt for Experiments

Description

## Statistical Analysis for Experiments

Analyze the following experiment:

**Experiment:** [EXPERIMENT_DESCRIPTION]
**Hypothesis:** [HYPOTHESIS — H0 and H1]
**Data:** [DATA_SUMMARY — sample sizes, means, etc.]
**Significance Level:** [ALPHA — 0.05/0.01/0.10]

**Analysis Framework:**

### 1. Test Selection
Based on your data characteristics:

| Question | Your Data | Recommended Test |
|---|---|---|
| Comparing 2 group means? | Normal, independent | Independent t-test |
| Comparing 2 group means? | Non-normal | Mann-Whitney U test |
| Comparing 2 paired measurements? | Normal | Paired t-test |
| Comparing 3+ group means? | Normal | One-way ANOVA |
| Testing proportions? | Large sample | Chi-square test |
| Correlation between variables? | Continuous | Pearson/Spearman |
| Predicting outcome? | Continuous | Linear regression |

### 2. Assumptions Check
- Normality: Shapiro-Wilk test + Q-Q plot
- Homogeneity of variance: Levene's test
- Independence: Study design verification
- Sample size adequacy: Power analysis

### 3. Python Implementation
```python
from scipy import stats
import numpy as np

# [APPROPRIATE_TEST] implementation
# Include: test statistic, p-value, confidence interval, effect size
```

### 4. Results Interpretation
- Test statistic: [VALUE]
- P-value: [VALUE] → [SIGNIFICANT/NOT_SIGNIFICANT] at α = [ALPHA]
- Effect size: [VALUE] → [SMALL/MEDIUM/LARGE] (Cohen's d / eta-squared)
- Confidence interval: [LOWER, UPPER]
- Practical significance: [INTERPRETATION]

### 5. Reporting
"A [TEST_NAME] revealed a [SIGNIFICANT/NON-SIGNIFICANT] [DIRECTION] in [DV] between [GROUP_A] (M = [MEAN], SD = [SD]) and [GROUP_B] (M = [MEAN], SD = [SD]), t([DF]) = [T], p = [P], d = [D]."

**Variables:** Replace [EXPERIMENT_DESCRIPTION], [HYPOTHESIS], [DATA_SUMMARY], [ALPHA].

Free to copy and use. Compatible with Claude 4 Opus, GPT-5, Gemini 2.5 Pro.

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