| Title |
An exhaustive power comparison of normality tests |
| Authors |
Arnastauskaitė, Jurgita ; Ruzgas, Tomas ; Bražėnas, Mindaugas |
| DOI |
10.3390/math9070788 |
| Full Text |
|
| Is Part of |
Mathematics.. Basel : MDPI. 2021, vol. 9, iss. 7, art. no. 788, p. 1-20.. ISSN 2227-7390 |
| Keywords [eng] |
goodness of fit test ; normal distribution ; power comparison |
| Abstract [eng] |
A goodness-of-fit test is a frequently used modern statistics tool. However, it is still unclear what the most reliable approach is to check assumptions about data set normality. A particular data set (especially with a small number of observations) only partly describes the process, which leaves many options for the interpretation of its true distribution. As a consequence, many goodness-of-fit statistical tests have been developed, the power of which depends on particular circumstances (i.e., sample size, outlets, etc.). With the aim of developing a more universal goodness-of-fit test, we propose an approach based on an N-metric with our chosen kernel function. To compare the power of 40 normality tests, the goodness-of-fit hypothesis was tested for 15 data distributions with 6 different sample sizes. Based on exhaustive comparative research results, we recommend the use of our test for samples of size n≥118. |
| Published |
Basel : MDPI |
| Type |
Journal article |
| Language |
English |
| Publication date |
2021 |
| CC license |
|
