Compute the standard score of a value given the dataset mean and standard deviation for cross-scale comparison and outlier detection.
| Z 分数范围 | 百分位近似 | 等级描述 |
|---|---|---|
| Z ≥ 2.0 | ≥ 97.7% | 优秀(远超平均) |
| 1.0 ≤ Z < 2.0 | 84.1%–97.7% | 良好(高于平均) |
| -1.0 ≤ Z < 1.0 | 15.9%–84.1% | 中等(接近平均) |
| -2.0 ≤ Z < -1.0 | 2.3%–15.9% | 偏低(低于平均) |
| Z < -2.0 | < 2.3% | 较低(远低于平均) |
| Item | Formula / Note | Source / Standard |
|---|---|---|
| Z-score definition | Z = (x − μ) ÷ σ, the distance from the mean in SD units | Pearson 1896 standardization |
| T-score and percentile | T = 50 + 10Z (avoids negatives); Z = 1.645 is the 95th percentile (one-sided) | Standard educational-measurement conversions |
| Norm referencing | Z-scores are comparable only within the same norm group; align norms before comparing | AERA/APA/NCME Testing Standards |
| Normal approximation | Large samples approach normality: ±1σ 68.3%, ±2σ 95.4%, ±3σ 99.7% | 68-95-99.7 empirical rule |
Z 分数(标准分数)用于衡量某个数值相对于数据集平均水平的偏离程度。Z = 0 表示等于平均值,Z 为正表示高于平均,Z 为负表示低于平均。
学习与教学场景下的快速计算。