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Use when planning or reporting statistical analysis - provides test selection, execution code, and APA format guidelines
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Use when writing academic papers, theses, or research articles - supports brainstorming, chapter writing, literature review, and LaTeX output
Use for translation, polishing, or de-AI-ification of academic text - provides ready-to-use prompt templates
Use when writing or revising academic papers, especially Chinese journal manuscripts, that need natural prose, de-AI-ification, Markdown formatting, or quality checks
Use when writing or revising Introduction, Related Work, background, literature synthesis, or any section where references must drive claims
Use when designing experiments, result tables, mock planning data, evaluation protocols, or results sections before real data are final
Use when creating data visualizations for papers - generates publication-quality plots with top-journal color schemes
Basado en la clasificación ocupacional SOC
| name | statistical-analysis |
| description | Use when planning or reporting statistical analysis - provides test selection, execution code, and APA format guidelines |
本技能提供学术论文中统计分析的选择、执行和报告指南。
| 数据特征 | 推荐检验 |
|---|---|
| 独立、连续、正态 | 独立样本t检验 |
| 独立、连续、非正态 | Mann-Whitney U检验 |
| 配对、连续、正态 | 配对样本t检验 |
| 配对、连续、非正态 | Wilcoxon符号秩检验 |
| 二分类结果 | 卡方检验或Fisher精确检验 |
| 数据特征 | 推荐检验 |
|---|---|
| 独立、连续、正态 | 单因素方差分析 |
| 独立、连续、非正态 | Kruskal-Wallis检验 |
| 配对、连续、正态 | 重复测量方差分析 |
| 配对、连续、非正态 | Friedman检验 |
| 分析目标 | 推荐方法 |
|---|---|
| 两个连续变量关系 | Pearson相关（正态）或Spearman相关（非正态） |
| 连续结果与预测变量 | 线性回归 |
| 二分类结果与预测变量 | 逻辑回归 |
from scipy import stats
# Shapiro-Wilk检验（样本量<5000）
stat, p_value = stats.shapiro(data)
print(f"Shapiro-Wilk检验: W={stat:.4f}, p={p_value:.4f}")
if p_value > 0.05:
print("数据符合正态分布假设")
else:
print("数据不符合正态分布，考虑使用非参数检验")
from scipy import stats
# Levene检验
stat, p_value = stats.levene(group1, group2)
print(f"Levene检验: F={stat:.4f}, p={p_value:.4f}")
if p_value > 0.05:
print("方差齐性假设满足")
else:
print("方差不齐，使用Welch's t检验")
| 检验 | 效应量 | 小 | 中 | 大 |
|---|---|---|---|---|
| t检验 | Cohen's d | 0.20 | 0.50 | 0.80 |
| ANOVA | η²_p | 0.01 | 0.06 | 0.14 |
| 相关 | r | 0.10 | 0.30 | 0.50 |
| 回归 | R² | 0.02 | 0.13 | 0.26 |
import pingouin as pg
# t检验返回Cohen's d
result = pg.ttest(group1, group2)
d = result['cohen-d'].values[0]
print(f"Cohen's d = {d:.2f}")
# ANOVA返回偏η²
aov = pg.anova(dv='score', between='group', data=df)
eta_p2 = aov['np2'].values[0]
print(f"Partial η² = {eta_p2:.3f}")
A组（n = 48, M = 75.2, SD = 8.5）得分显著高于B组
（n = 52, M = 68.3, SD = 9.2），t(98) = 3.82, p < .001,
d = 0.77, 95% CI [0.36, 1.18]。
单因素方差分析显示处理条件对测试分数有显著主效应，
F(2, 147) = 8.45, p < .001, η²_p = .10。事后比较使用
Tukey HSD表明，条件A（M = 78.2, SD = 7.3）得分显著
高于条件B（M = 71.5, SD = 8.1, p = .002）。
多元线性回归预测考试成绩，整体模型显著，
F(3, 146) = 45.2, p < .001, R² = .48。学习时间
（β = .35, p < .001）和先前GPA（β = .28, p < .001）
是显著预测变量。
import numpy as np
import pingouin as pg
from scipy import stats
# 数据
group_a = np.array([75, 82, 68, 79, 85, 72, 88, 76])
group_b = np.array([65, 70, 62, 68, 75, 60, 72, 66])
# 1. 描述统计
print(f"A组: M={group_a.mean():.2f}, SD={group_a.std():.2f}")
print(f"B组: M={group_b.mean():.2f}, SD={group_b.std():.2f}")
# 2. 正态性检验
_, p_a = stats.shapiro(group_a)
_, p_b = stats.shapiro(group_b)
print(f"正态性: A组 p={p_a:.3f}, B组 p={p_b:.3f}")
# 3. t检验
result = pg.ttest(group_a, group_b)
print(f"t = {result['T'].values[0]:.2f}")
print(f"p = {result['p-val'].values[0]:.4f}")
print(f"Cohen's d = {result['cohen-d'].values[0]:.2f}")