---
title: "Statistical Analysis"
type: concept
tags: [statistics, quality, data-science]
sources: [testing-test-results-analyzer]
last_updated: 2026-04-28
---

## Aliases
- Statistical Methods
- Quantitative Analysis
- Confidence Interval Analysis

## Definition

统计分析——使用数学和统计方法从测试数据中提取洞察、验证假设、计算置信区间，确保所有结论有统计依据而非主观推断。

## Core Methods (from TestResultsAnalyzer)

```python
import numpy as np
from scipy import stats

# 置信区间计算
confidence_level = 0.95
n = len(sample_data)
mean = np.mean(sample_data)
std_err = stats.sem(sample_data)
ci = stats.t.interval(confidence_level, n-1, loc=mean, scale=std_err)

# 假设检验
t_stat, p_value = stats.ttest_ind(group_a, group_b)
significant = p_value < 0.05

# 相关性分析
correlation, p_val = stats.pearsonr(x, y)
```

## Key Principles

- **95% Confidence Level**：默认使用 95% 置信水平，所有结论必须报告置信区间。
- **Statistical Significance Required**：相关性和差异分析必须伴随 p-value 检验。
- **Cross-Validation**：重要结论需多数据源交叉验证。
- **Methodology Documentation**：分析方法必须记录以确保可复现。

## Key Rules (from TestResultsAnalyzer)

> "Always use statistical methods to validate conclusions and recommendations"
> "Provide confidence intervals and statistical significance for all quality claims"
> "Base recommendations on quantifiable evidence rather than assumptions"

## Connections

- [[Quality-Metrics]]：统计方法是质量指标计算的基础。
- [[Failure-Pattern-Analysis]]：失败模式识别依赖统计方法。
- [[Defect-Prediction]]：预测模型需要统计验证。
- [[Release-Readiness-Assessment]]：发布决策依赖统计置信度。
- [[Quality-ROI-Analysis]]：ROI 计算需要统计方法。