Presumably, because his data does not represent some quantity to approximate normal distribution.
Transforming the D with f = 1 / x yields:
| Row | Value | Z | Significant Outlier? |
|---|---|---|---|
| 1 | 10.000000000000000000 | 1.78885438120805620000 | Significant outlier. P < 0.05 |
| 2 | 0.000666666666666667 | 0.44717876261411860000 | |
| 3 | 0.000588235294117647 | 0.44719630129821775000 | |
| 4 | 0.000476190476190476 | 0.44722135656121640000 | |
| 5 | 0.000312500000000000 | 0.44725796073450363000 |
Transforming the D with f = log x gives:
| Row | Value | Z | Significant Outlier? |
|---|---|---|---|
| 1 | -2.30258509299405 | 1.7851028840345886 | Significant outlier. P < 0.05 |
| 2 | 7.31322038709030 | 0.3777123990128823 | |
| 3 | 7.43838353004431 | 0.4058644616784443 | |
| 4 | 7.64969262371151 | 0.4533927252416877 | |
| 5 | 8.07090608878782 | 0.5481332981015742 |
In reply to Re^2: How to best eliminate values in a list that are outliers
by Anonymous Monk
in thread How to best eliminate values in a list that are outliers
by Anonymous Monk
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