| Value | z-score | |z| > 2 ? |
|---|---|---|
| 12 | -1.588 | — |
| 15 | -1.325 | — |
| 18 | -1.063 | — |
| 22 | -0.713 | — |
| 25 | -0.450 | — |
| 27 | -0.275 | — |
| 28 | -0.188 | — |
| 30 | -0.013 | — |
| 33 | 0.250 | — |
| 35 | 0.425 | — |
| 40 | 0.863 | — |
| 42 | 1.038 | — |
| 45 | 1.300 | — |
| 50 | 1.738 | — |
Use ‘sample’ variance (divide by n−1, Bessel's correction) whenever your data is a subset of a larger group — almost always in real-world studies.
‘Population’ variance (divide by n) only applies when you have the entire universe (e.g. every employee's salary at a small company).
Skewness measures asymmetry; kurtosis measures tail heaviness. Both help spot non-normal data before applying tests that assume normality.
Use ‘sample’ variance (divide by n−1, Bessel's correction) whenever your data is a subset of a larger group — almost always in real-world studies.
‘Population’ variance (divide by n) only applies when you have the entire universe (e.g. every employee's salary at a small company).
Skewness measures asymmetry; kurtosis measures tail heaviness. Both help spot non-normal data before applying tests that assume normality.
| Value | z-score | |z| > 2 ? |
|---|---|---|
| 12 | -1.588 | — |
| 15 | -1.325 | — |
| 18 | -1.063 | — |
| 22 | -0.713 | — |
| 25 | -0.450 | — |
| 27 | -0.275 | — |
| 28 | -0.188 | — |
| 30 | -0.013 | — |
| 33 | 0.250 | — |
| 35 | 0.425 | — |
| 40 | 0.863 | — |
| 42 | 1.038 | — |
| 45 | 1.300 | — |
| 50 | 1.738 | — |