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Published: 2023-06-26

The Simulation Study of Normality Test Using Kolmogorov-Smirnov, Anderson-Darling, and Shapiro-Wilk

Institut Teknologi Sepuluh Nopember
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Giatma Dwijuna Ahadi

Departemen Statistika, Fakultas Sains dan Analitika Data, Institut Teknologi Sepuluh Nopember, Surabaya
Institut Teknologi Sepuluh Nopember, Surabaya
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Neni Nur Laili Ersela Zain

Departemen Matematika, Fakultas Sains dan Analitika Data, Institut Teknologi Sepuluh Nopember, Surabaya
Normal Distribution Kolmogorov-Smirnov Anderson-Darling Shapiro-Wilk

Abstract

The normal distribution is an important assumption for many statistical methods. The t-distribution is similar to the normal distribution, but there are differences in variance and free degree depending on the sample size. Normality testing usually uses Kolmogorov-Smirnov, Anderson-Darling, and Shapiro-Wilk tests. Simulating was performed on data derived from normal distributions, t-distributions, and exponential distributions. In the N(10,2) generation data, it was found that the Shapiro-Wilk method was better than other methods, while in a large sample, it was found that the Anderson-Darling method was better than the other methods. The data for the generation of the distribution of t(1) shows that the cumulative value of rejecting  is close to 100%, meaning the data is not normally distributed. In a near-normal t(20) distribution, but not a normal data gain, Anderson-Darling and Shapiro-Wilk test performance showed good results. Meanwhile, in the t(100) generation data, the result was obtained that the most consistent cumulative value was the Anderson-Darling method. Furthermore, the Exp(1) data generator produces a cumulative value of rejecting  close to 100%, with the most consistent method being Kolmogorov-Smirnov. So it can be known that the normality test selection depends on the number of samples and the data distribution.

References

  1. Ahmad, F. & Sherwani, R. A. K. (2015). Power Comparison of Various Normality Tests. Pak.j.stat.oper.res, XI(3), 331-345.
  2. Ardiansah & Supardi U.S. (2022). Statistika Ekonomi 1 (Edisi Revisi). Widina Bhakti Persada : Bandung.
  3. Bain L.J & Max, E. (1991). Introduction To Probability and Mathematical Statistics. California: Duxbury.
  4. Bain, L.J. & Engelhardt M. (1992). Introduction to Probability and Mathematical Statistics (2nd ed.). United State Of America: Duxburry.
  5. Biu, E.O., Nwakuya, M.T., & Wonu, N. (2019). Detection of Non-Normality in Data Sets and Comparison between Different Normality Tests. Asian Journal of Probability and Statistics, 5(4), 1-20.
  6. Blangiardo, M. & Cameletti, M. (2015). Spatial and Spatio-temporal Bayesian Models with R - INLA. Chichester: John Wiley & Sons.
  7. Cohen, J. (1988). Statistical PowerAanalysis for The Behavioral Sciences. Hillsdale, NJ: Erlbaum.
  8. D’Agostino, R.B. & Stephens, M.A. (1986): Goodness-of-fit techniques. New York, Marcel Dekker.
  9. Dahlan, M.S. (2009). Statistik untuk Kedokteran dan Kesehatan, Edisi 4 (Deskriptif, Bivariat dan Multivariat, dilengkapi Aplikasi dengan Menggunakan SPSS). Jakarta: Salemba Medika.
  10. Denis, D.J. (2021). Applied Univariate, Bivariate, And Multivariate Statistics : Understanding Statistics for Social and Natural Scientists, with Applications in SPSS and R. USA: John Wiley & Sons.
  11. Farrel, P. J & Stewart, K. R. (2006). Comprehensive Study of Tests for Normality and Symmetry; Extending the Spiegelhalter Test. Journal of Statistical Computation and Simulation, 76(9), 803-816.
  12. Gosset, W. S. (1908). The Probable Error of a Mean. Biometrika, 6(1), 1-25.
  13. Gupta & Kundu. (1999). Generalisasi Exponential Distributions. Austral & Zealand J. Statist, 2(41), 173-188.
  14. Karjono, A. & Wijaya. (2017). Analisis Pengaruh Roe, Der dan Tato Terhadap Harga Saham Pada Perusahaan Manufaktur Sektor Industri Barang Konsumsi yang Terdaftar di Bursa Efek Indonesia Periode 2012–2015. ESENSI. 20(2).
  15. Kissell, R., & Poserina, J. (2017). Advanced Math and Statistics, Chapter 4. Optimal Sports Math, Statistics, and Fantasy. Elsevier.
  16. Kolmogorov, A. N. (1933). Sulla Determinazione Empirica di Une Legge di Distribuzione. Giornale dell’Intituo Italiano degli Attuari, 4, 83-91.
  17. Law, A.M. & Kelton W.D. (1991). Simulation Modeling and Analysis. McGraw- Hill.
  18. Lukiastuti, F. & Hamdani, M. (2012). Statistika Non Parametris. Yogyakarta: CAPS.
  19. Mbah, A.K. & Paothong, A. (2014). Shapiro-Francia Test Compared to Other Normality Test Using Expected pValue. Journal of Statistical Computation and Simulation, 85(15), 3002-3016.
  20. Nosakhare, U. H., Bright, A. F. (2017). Evaluating of Techniques for Univariate Normality Test using Monte Carlo Simulation. American Journal of Theoritical and Applied Statistic, 6(5-1), 51-61.
  21. Poletiek, F. H. (2013). Hypothesis-Testing Behaviour. Psychology Press.
  22. Purwanto. (2011). Statistik untuk Penelitian. Yogyakarta: Pustaka Pelajar.
  23. Putri, R. D. (2020). Perbandingan Kekuatan Uji Metode Kolmogorov-Smirnov Anderson-Darling dan Shapiro-Wilk untuk Menguji Normalitas Data. Yogyakarta: Universitas Sanata Dharma.
  24. Razali, N.M. & Wah, Y.B. (2011). Power Comparision of Shapiro-Wilk, Kolmogorov-Smirnov, Lilliefors, and Anderson-Darling tests. Journal of Statistical modeling and Analytics 2(1), 21-33.
  25. Sauddin, A. (2014). Pengenalan R Programming (Bagian I). Matematika dan Statistika serta Aplikasinya, 2(2), 50-59.
  26. Sebayang, M. (2004). Probabilitas dan Statistik dalam Ilmu Rekayasa. Pekanbaru: Teknik Sipil FT UR.
  27. Shapiro, S. S. & Wilk, M.B. (1965). An Analysis of Variance Test for Normality (Complete Samples). Biometrika, 52(3/4), 591—611.
  28. Sintia I., Pasarella, M.D., & Nohe, D.A. (2022). Perbandingan Tingkat Konsistensi Uji Distribusi Normalitas Pada Kasus Tingkat Pengangguran di Jawa. Prosiding Seminar Nasional Matematika, Statistika, dan Aplikasinya Terbitan II, Samarinda, Indonesia, 322-333.
  29. Stephens, M. A. (1974). EDF Statistics for Goodness of Fit and Some Comparisons. Journal of The American Statistical Association, 69, 730-737.
  30. Taeger, D., & Kuhnt, S. (2014). Statistical Hypothesis Testing with SAS and R. Wiley Online Library.
  31. Walpole, R. E., Myers, R. H., Myers, S. L., & Ye, K. (2011). Probability and Statistics for Engineers and Scientists (9th ed.). United State of America: Prentice Hall.
  32. Wirawan, Nata. (2016). Statistika Ekonomi dan Bisnis (Statistika Deskriptif), Edisi 4. Bali: Keraras Emas Denpasar.
  33. Yam, J.H. (2020). Ambiguitas Statistika Deskriptif & Statistika Inferensial. Pelita : Jurnal Penelitian dan Karya Ilmiah, 20(2), 117-124.
  34. Zammaduita, A. (2013). Perbandingan Uji Kenormalan pada Kategori Fungsi Distribusi Empiris menggunakan Metode Simulasi Monte Carlo. Surakarta: Universitas Sebelas Maret.

How to Cite

Ahadi, G. D., & Zain, N. N. L. E. (2023). The Simulation Study of Normality Test Using Kolmogorov-Smirnov, Anderson-Darling, and Shapiro-Wilk. EIGEN MATHEMATICS JOURNAL, 6(1), 11–19. https://doi.org/10.29303/emj.v6i1.131