Application of statistical tests of data distribution and its usefulness in animal production

Aplicación de pruebas estadísticas de distribución de datos y su utilidad en producción animal

Introduction: The appropriate choice of statistical tools for inferential data analysis is fundamental in science. Thus, identifying the behavior of the observations is essential; to select, with the greatest possible precision, the statistical technique that leads to accurate results and enriching conclusions. Objective:The distribution of raw and residual data from cattle and chicken farming was studied by verifying parametric assumptions; In turn, three statistical methods were compared, by zootechnical species, discussing their plasticity, adjustment and precision. Materials and methods: The following were analyzed in cattle: body condition, live weight, hair length and biochemical constants (calcium, phosphorus, magnesium). In chickens: live weight, breast width, thigh length, crest length, presence of endo and ectoparasites. Tests of normality (Shapiro Wilk and Kolmogorov (Lilliefors)) and homogeneity of variances (Levene) were applied. The inferential methods were considered in bovines: ANOVA with Tukey; Welch’s ANOVA with the Games Howell test and Kruskal Wallis with Dunn’s test. In birds: the student test, with Welch and Wilcoxon-Mann-Whitney correction. Results: Normality tests maintained similar results. A difference was found in decision criteria between the inferential analyses, for magnesium level and thigh length. Conclusions: It is explicitly recommended, in veterinary and zootechnical studies, with scientific rigor, to analyze the normality and homogeneity of variance, to appropriately identify and know the behavioral pattern of the data coming from the work, in order to properly implement the inferential statistical tool. that will contribute to discriminating chance and causality in the events treated

1. Amini-Seresht, E., & Milošević, B. (2020). New non-parametric tests for independence. Journal of Statistical Computation and Simulation, 90(7), 1301-1314. https://doi.org/10.1080/00949655.2020.1725007
2. Barrios, Y. D., Guerrero, Z. E., Zambrano, D. F., & Ponce Solórzano, H. X. (2022). Análisis estadístico cuando no se cumplen los supuestos de las pruebas paramétricas, en el contexto de la investigación de la Cultura Física. Universidad y Sociedad,14(S1), 591-600.
3. Barton, N. (2017). How does epistasis influence the response to selection? Heredity,118, 96-109. https://doi.org/10.1038/hdy.2016.109
4. Blanca, M. J., Alarcón, R., Arnau, J., Bono, R., & Bendayan, R. (2017). Non-normal data: is ANOVA still a valid option? Psicothema, 29(4), 552-557. https://doi.org/10.7334/psicothema2016.383
5. Blanca, M. J., Alarcón, R., Arnau, J., Bono, R., & Bendayan, R. (2018). Effect of variance ratio on anova robustness: Might 1.5 be the limit? Behavior Research Methods, 50(3), 937-962. https://doi.org/10.3758/s13428-017-0918-2
6. Blanca, M. J., Arnau, J., García-Castro, F. J., Alarcón, R., & Bono, R. (2023). Non-normal data in repeated measures ANOVA: Impact on type I error and power. Psicothema, 35(1), 21-29. https://doi.org/10.7334/psicothema2022.292
7. Borges, P., Mendoza, Z., & Morais, P. (2019). Redes neurales artificiales para estimar las pérdidas en la producción lechera. Archivos de Zootecnia, 68(262), 206-212. https://doi.org/10.21071/az.v68i262.4138
8. Cabrera, G., Zanazzi, J. F., Zanazzi, J. L., & Boaglio, L. (2017). Comparación de potencias en pruebas estadísticas de normalidad, con datos escasos. Revista de la Facultad de Ciencias Exactas, Físicas y Naturales, 4(2), 47-52.
9. Centurión, L. (2018). Caracterización de las herramientas estadísticas empleadas en las tesis de grado en el área de la zootecnia[Tesis de Maestría, Universidad Nacional de Asunción]. www.conacyt.gov.py
10. Delacre, M., Lakens, D., & Leys, C. (2017). Why psychologists should by default use Welch’s t-test instead of Student’s t-test. International Review of Social Psychology, 30(1), 92-101. https://doi.org/10.5334/irsp.82
11. Fernández-Lozano, C., Gestal, M., Munteanu, C. R., Dorado, J., & Pazos, A. (2016). A methodology for the design of experiments in computational intelligence with multiple regression models. PeerJ, 4, 1-22. https://doi.org/10.7717/peerj.2721
12. Flores-Muñoz, P., Muñoz, L., & Sánchez, T. (2019). Estudio de potencia de pruebas de normalidad usando distribuciones desconocidas con distintos niveles de no normalidad. Revista Perfiles, 1(21), 4-11.
13. Galeano, L., Galván, I., Aler, R., & Cerón Muñoz, M. F. (2018). Forecasting egg production curve with neural networks. Archivos de Zootecnia, 67(257), 81-86.
14. Hadi, A. N. (2018). A comprehensive study on power of tests for normality. Journal of Statistical Theory and Applications, 17(4), 647-660. https://doi.org/10.2991/jsta.2018.17.4.7
15. Hernández Barajas, F., Cano Urrego, B.& Caicedo Chamorro, E. (2021). Modelos GAMLSS para analizar el grado secado de calcio dihidratado. Revista EIA, 18(35), 1-13. https://doi.org/10.24050/reia. v18i35.1439
16. Herrera Villafranca, M., Galindo Blanco, J., Padilla Corrales, C., Guerra Bustillo, C., Medina Mesa, Y., & Sarduy García, Lucia. (2020). Propuesta del modelo lineal mixto y generalizado mixto para el análisis de un experimento de la microbiología del rumen. Cuban Journal of Agricultural Science, 54(2), 149-156. http://scielo.sld.cu/scielo.php?script=sci_arttext&pid=S2079-34802020000200149&lng=es&tlng=es.
17. Hopkins, S., Dettori, J. R., & Chapman, J. R. (2018). Parametric and nonparametric tests in spine research: Why do they matter? Global Spine Journal, 8(6), 652-654. https://doi.org/10.1177/219256821878267
18. Iglesias, C., Navas González, F. J., Ciani, E., Camacho Vallejo, M. E., & Delgado Bermejo, J. V. (2022). Bayesian linear regression and natural logarithmic correction for digital image-based extraction of linear and tridimensional zoometrics in dromedary camels. Mathematics, 10(19), 1-24. https://doi.org/10.3390/math10193453
19. Kim, T., & Park, J. (2019). More about the basic assumptions of t-test: normality and sample size. Korean Journal of Anesthesiology, 72(4), 331-335. https://doi.org/10.4097/kja.d.18.00292
20. Kim, Y., & Cribbie, R. (2018). ANOVA and the variance homogeneity assumption: Exploring a better gatekeeper. British Journal of Mathematical and Statistical Psychology, 71(1), 1-12. https://doi.org/10.1111/bmsp.12103
21. Le Boedec, K. (2016). Sensitivity and specificity of normality tests and consequences on reference interval accuracy at small sample size: a computer-simulation study. Veterinary Clinical Pathology, 45(4), 648–656. https://doi.org/10.1111/vcp.12390
22. Lee, S., & Lee, D. K. (2018). What is the proper way to apply the multiple comparison test? Korean Journal of Anesthesiology, 71(5), 353-360. https://doi.org/10.4097/kja.d.18.00242
23. Martins-Bessa, A., Quaresma, M., Leiva, B., Calado, A., & Navas González, F. J. (2021). Bayesian linear regression modelling for sperm quality parameters using age, body weight, testicular morphometry, and combined biometric indices in donkeys. Animals, 11(1), 1-23. https://doi.org/10.3390/ani11010176
24. Martínez-López, R. (2017). Métodos estadísticos aplicados en Zootecnia. Etigraf.Midway, S., Robertson, M., Flinn, S., & Kaller, M. (2020). Comparing multiple comparisons: practical guidance for choosing the best multiple comparisons test. PeerJ, 8, 1-26. https://doi.org/10.7717/peerj.10387
25. Mircioiu, C., & Atkinson, J. (2017). A Comparison of Parametric and non-parametric methods applied to a Likert scale. Pharmacy, 5(2), 26. https://doi.org/10.3390/pharmacy5020026
26. Mishra, P., Pandey, C. M., Singh, U., Gupta, A., Sahu, C., & Keshri, A. (2019). Descriptive statistics and normality tests for statistical data. Annals of Cardiac Anaesthesia, 22(1), 67-72. https://doi.org/10.4103/aca.ACA_157_18
27. Molina, R. S., Rix, G., Mengiste, A. A., Álvarez, B., Seo, D., Chen, H., Hurtado, J. E., Zhang, Q., García García, J. D., Heins, Z. J., Almhjell, P. J., Arnold, F. H., Khalil, A. S., Hanson, A. D., Dueber, J. E., Schaffer, D. V., Chen, F., Kim, S., Fernández, L. Á., & Liu, C. C. (2022). In vivo hypermutation and continuous evolution. Nature Reviews Methods Primers, 2(36). 1-22. https://doi.org/10.1038/s43586-022-00119-5
28. Pataky T. C., Yagi, M., Ichihashi, N., & Cox P. G. (2021). Landmark-free, parametric hypothesis tests regarding two-dimensional contour shapes using coherent point drift registration and statistical parametric mapping. PeerJ Computer Science, 7, 1-25. https://doi.org/10.7717/peerj-cs.542
29. Pelea, L. P. (2018). ¿Cómo proceder ante el incumplimiento de las premisas de los métodos paramétricos? o ¿cómo trabajar con variables biológicas no normales? Revista Del Jardín Botánico Nacional, 39, 1-12.
30. Pelea, L. P. (2019). Valores atípicos en los datos, ¿cómo identificarlos y manejarlos? Revista Del Jardín Botánico Nacional, 40, 99-107.
31. R Core Team. (2020). A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria. https://www.R-project.org/
32. Ruffing, A. M., Anthony, S. M., Strickland, L. M., Lubkin, I., & Dietz, C. R. (2021). Identification of metal stresses in arabidopsis thaliana using hyperspectral reflectance imaging. Frontiers in Plant Science, 12, 1-13. https://doi.org/10.3389/fpls.2021.624656
33. Sánchez-Espigares, J. A, Grima, P., & Marco-Almagro, L. (2018). Visualizing type II error in normality tests. American Statistician, 72(2), 158-162. https://doi.org/10.1080/00031305.2016.1278035
34. Sauder, D. C., & DeMars, C. E. (2019). An updated recommendation for multiple comparisons. Advances in Methods and Practices in Psychological Science, 2(1), 26-44. https://doi.org/10.1177/2515245918808784
35. Siegel, S., & Castellan, N. (2015). Estadística no paramétrica: aplicada a las ciencias de la conducta. Trillas.Vetter T. R. (2017). Fundamentals of Research Data and Variables: The Devil Is in the Details. Anesthesia & Analgesia, 125(4), 1375-1380. https://doi.org/10.1213/ANE.0000000000002370
36. Wijekularathna, D. K., Manage, A. B. W., & Scariano, S. M. (2022). Power analysis of several normality tests: A Monte Carlo simulation study. Communications in Statistics–Simulation and Computation, 51(3), 757-773. https://doi.org/10.1080/03610918.2019.1658780