13. Experimental Results

4. Experimental Results

4.1 End-to-End Performance (supports main claim)

4.2 Ablation Study (supports claim that components matter)

4.3 Error Analysis (supports claim about failure modes)

4.4 Scaling Behavior (supports claim about generalization)

This structure makes it easy for readers to find evidence for specific claims.

### Quantitative Presentation

Use tables for comparisons with baselines:

```python
# Results table for paper
results_table = """
| System | Accuracy | Latency (ms) | Memory (GB) |
|--------|----------|--------------|-------------|
| Baseline | 91.8% | 234 | 8.2 |
| Ours | 94.2% | 198 | 7.1 |
| +Compression | 94.0% | 145 | 4.3 |
| +Pruning | 93.1% | 89 | 2.8 |
"""

Use figures for trends and distributions:

import matplotlib.pyplot as plt

# Scaling behavior visualization
def plot_scaling_results():
    fig, axes = plt.subplots(1, 2, figsize=(10, 4))
    
    # Left: accuracy vs dataset size
    axes[0].plot(dataset_sizes, accuracies, 'o-', label='Ours')
    axes[0].plot(dataset_sizes, baseline_accuracies, 's--', label='Baseline')
    axes[0].set_xlabel('Training Data Size')
    axes[0].set_ylabel('Accuracy')
    axes[0].legend()
    
    # Right: latency vs batch size (log-log)
    axes[1].loglog(batch_sizes, latencies, 'o-', label='Ours')
    axes[1].loglog(batch_sizes, baseline_latencies, 's--', label='Baseline')
    axes[1].set_xlabel('Batch Size')
    axes[1].set_ylabel('Latency (ms)')
    
    plt.tight_layout()
    plt.savefig('figures/scaling_results.pdf')

Statistical Rigor

AI systems have inherent variance. Report uncertainty:

• Standard deviation: For repeated runs with different random seeds
• Confidence intervals: For estimated population parameters
• Statistical tests: For comparing systems (t-test, bootstrap)

# Calculate and report confidence intervals
def report_with_ci(values, confidence=0.95):
    import numpy as np
    from scipy import stats
    
    mean = np.mean(values)
    sem = stats.sem(values)  # Standard error of mean
    ci = stats.t.interval(confidence, len(values)-1, loc=mean, scale=sem)
    
    return f"{mean:.2f} ± {sem * 1.96:.2f} (95% CI)"

Handling Negative Results

Don't hide experiments where your approach didn't win. Negative results are valuable information:

"Surprisingly, adding retrieval augmentation decreased performance on tasks requiring precise factual recall (Table 4, rows 3-4). Analysis reveals that retrieved passages occasionally contained contradictory information that confused the model. We address this in Section 5.2."