Natalia Loaiza Velásquez

^{1}, María Isabel González Lutz

^{2}& Julián Monge-Nájera

^{3}

1. Biología Tropical, Universidad de Costa Rica, 2060 San José, Costa Rica; natalia.loaiza@ucr.ac.cr

2. Escuela de Estadística, Universidad de Costa Rica, 2060 San José, Costa Rica; mariaisabel.gonzalezlutz@ucr.ac.cr

3. Laboratorio de Ecología Urbana, UNED, 2050 San José, Costa Rica; Universidad de Costa Rica, 2060 San José, Costa Rica; julianmonge@gmail.com

Dirección para correspondencia

Abstract

Tropical biologists study the richest and most endangered biodiversity in the planet, and in these times of climate change and mega-extinctions, the need for efficient, good quality research is more pressing than in the past. However, the statistical component in research published by tropical authors sometimes suffers from poor quality in data collection; mediocre or bad experimental design and a rigid and outdated view of data analysis. To suggest improvements in their statistical education, we listed all the statistical tests and other quantitative analyses used in two leading tropical journals, the Revista de Biología Tropical and Biotropica, during a year. The 12 most frequent tests in the articles were: Analysis of Variance (ANOVA), Chi-Square Test, Student’s T Test, Linear Regression, Pearson’s Correlation Coefficient, Mann-Whitney U Test, Kruskal-Wallis Test, Shannon’s Diversity Index, Tukey’s Test, Cluster Analysis, Spearman’s Rank Correlation Test and Principal Component Analysis. We conclude that statistical education for tropical biologists must abandon the old syllabus based on the mathematical side of statistics and concentrate on the correct selection of these and other procedures and tests, on their biological interpretation and on the use of reliable and friendly freeware. We think that their time will be better spent understanding and protecting tropical ecosystems than trying to learn the mathematical foundations of statistics: in most cases, a well designed one-semester course should be enough for their basic requirements. Rev. Biol. Trop. 59 (3): 983-992. Epub 2011 September 01.

Key words: education of tropical biologists, statistical courses, free statistics software, tropical biology journals.

Resumen

Los biólogos tropicales estudian la biodiversidad más rica y amenazada del planeta, y en estos tiempos de cambio climático y mega-extinción, la necesidad de investigación de buena calidad es más acuciante que en el pasado. Sin embargo, el componente estadístico en la investigación publicada por los autores tropicales adolece a veces de baja calidad en la toma de datos, mal diseño experimental y una visión anticuada del análisis de datos. Para sugerir mejoras en la enseñanza de la estadística, hicimos una lista de todas las pruebas estadísticas y otros análisis cuantitativos aplicados en dos de las principales revistas tropicales, la Revista de Biología Tropical y Biotropica, durante un año. Las 12 pruebas más frecuentes en los artículos fueron: Análisis de Varianza (ANDEVA), Chi-cuadrado, t de Student, Regresión lineal, Coeficiente de Correlación de Pearson, U de Mann-Whitney, Kruskal-Wallis, Índice de diversidad de Shannon, Prueba de Tukey, Análisis de Conclomerados, Correlación de Spearman y Análisis de Componentes Principales. Concluimos que la enseñanza de la estadística para los biólogos tropicales debe abandonar el viejo plan de estudios basado en el lado matemático de la estadística y concentrarse en (1) la correcta selección de estos y otros procedimientos y pruebas, (2) su interpretación biológica y (3) la utilización de programas de fácil uso. En la mayoría de los casos, un curso bien diseñado de un semestre bastaría para sus necesidades básicas.

Palabras clave: educación de biólogos tropicales, cursos de estadística, software libre para estadística, revistas de biología tropical.

Tropical biologists study the richest and often the most endangered biodiversity in the planet and in these times of climate change and mega-extinctions, the need for efficient, good quality research is more pressing than in the past. Previous authors have mentioned the need to improve the statistical training that tropical biologists receive, in order to reach those goals of efficiency and quality (Monge-Nájera 2002).

However, the statistical component in research published by tropical authors has been qualified as suffering from three problems: poor quality in data collection; mediocre or bad experimental design and a rigid and outdated view of data analysis (Fielding & Lauckner 1992).

In comparison with the scientific leaders, tropical statisticians are few, isolated and not always properly trained, having for example a poor grasp of the concept of variability and its usefulness in biology. Some scientific journals that publish in this field accept articles that lack appropriate descriptions of the experimental design and in some cases, 70 % of the articles have incorrect statistical analyses (Camacho 1997).

Some problems detected by the authors of Camacho’s (1997) compilation are not so serious today, thanks to the Internet, which provides acceptable statistical software for free and helps statisticians from tropical countries to keep abreast of new developments and in contact with colleagues. However, the key to the problem lies in education and progress is slow in this field. The results of statistics courses taught to biology students are often unsatisfactory, and students too often end up hating statistics and being unable to select and apply appropriate test and procedures (Camacho 1997).

To summarize the problem, tropical biologists are not being taught the statistics they really need. The objective of this article is to identify the statistical tests that are most frequently used in the current literature of tropical biology, and to use that information to recommend an updated syllabus to all institutions that educate tropical biologists.

Materials and methods

We listed all the statistical tests and other quantitative analyses used in two leading tropical journals, the Revista de Biología Tropical and Biotropica. For the Revista we reviewed all of the articles from editions 56-4 (December 2008) to 57-4 (December 2009), not including the supplements. For Biotropica, we used only the articles included in the “Papers” section from editions 40-4 (July 2008) to 41-4 (July 2009).

Results

We found that the articles in both journals used a large number of unusual statistics, but that each of these highly specialized procedures and tests was used very few times (Appendix).

The 12 most frequent tests in the articles were: Analysis of Variance (ANOVA), Chi-Square Test, Student’s T Test, Linear Regression, Pearson’s Correlation Coefficient, Mann-Whitney U Test, Kruskal-Wallis Test, Shannon’s Diversity Index, Tukey’s Test, Cluster Analysis, Spearman’s Rank Correlation Test and Principal Component Analysis (Table 1).

Discussion

In tropical ecosystems, a few species are very common, while most species are rare; and in our sample, the same applies to statistical procedures. There is no justification to burden students with a knowledge of procedures that they will seldom, if ever, use; if the need arises, such procedures can be applied by professional statisticians. On the other hand, the main mathematical procedures were almost the same in both journals, even though Biotropica is more limited to terrestrial ecology, while the Revista publishes articles from a variety of fields in marine and terrestrial biology, as well as on the conservation of tropical ecosystems.

This gives us some confidence to recommend that statistical education of tropical biologists be based on learning when to use the dozen tests that they are more likely to need in their professional life (top of Table 1). The mathematical procedures involved in those tests, as well as any mathematical proof to justify those procedures, must be eliminated from the courses and left to professional statisticians. Highly useful tests such as Chi-Squared and the G test are simple, but more complex ones –such as multivariate tests– can also be understood from a practical perspective without need for the subjacent mathematics.

Descriptive statistics is also something everyone needs, but again only to understand what averages, standard deviation and the like mean. Learning the algorithms to calculate them is not necessary because computers already “know” how to do it for us.

A frequent weakness in most articles, not only in tropical biology but in all fields and nations, is the poor selection of graphics that has become omnipresent after the introduction of computer spreadsheets. But this problem is easy to identify (a simple guide to good statistical graphics is here: www.biologiatropical.ucr.ac.cr).

Students should also learn that the statistician must be consulted before collecting the data, and not afterwards, when it may be too late. They should not finish their statistical courses hating statistics, as we know is the case in some institutions, but rather feeling confident that they can select the correct procedure and apply it, as also found by previous authors (Garfield and Ben-Zvi 2007, Metz 2008). ]]>

Software is another field in which current education clearly fails. Excellent free software is available to everyone in the Internet, yet most universities pay huge yearly amounts in licenses of professional software for students. Very good programs that are fit for tropical biologists are available in http://faculty.vassar.edu/lowry/VassarStats.html and in other addresses (e.g. http://statpages.org/, www.freestatistics.info and www.macstats.org). Professional programs should only be bought for professional statisticians.

Students should also learn that statistics has space for humor, as shown by Gary C. Ramseyer’s First Internet Gallery of Statistics Jokes (http://my.ilstu.edu/~gcramsey/Gallery. html), and that the motto of the guild is “Rubbish in, rubbish out”. They must always put mathematics at the service of biology and not the opposite; they should understand that only controlled experiments can identify cause and effect relationships; if this requirement is not met, no statistical procedure will (James & McCulloch 1990).

Additionally, it is of the greatest importance that:

1. Statistics be taught within the career’s subject courses (for example, as part of biology or medicine courses, instead of being a separate course) or at least, that all examples used in courses be from the career’s field (Metz 2008).

2. Related subjects, such as distribution, center and spread of data, be learned as a single concept (Garfield & Ben-Zvi 2007).

Currently, students can pass statistics courses with good marks even when they cannot understand statistics (Garfield & Ben-Zvi 2007, Metz 2008). Furthermore, they cannot apply statistical procedures outside the contexts in which they learned them and cannot understand the meaning of statistical tests and graphics (Garfield & Ben-Zvi 2007).

Every year unnecessary statistics textbooks continue to be written and published everywhere, despite the fact that all the information needed by students is freely available in Internet. Apart from that, almost all statistics textbooks available today are basically the same, a situation that would not be so bad if these they had the information that the students need, but a comparison of contents in books being published now and those from half a century ago will show little difference except for the addition of multivariate tests. Not surprisingly, student learning also is poor practically everywhere (Garfield & Ben-Zvi 2007, Metz 2008).

In conclusion, statistical education for tropical biologists must abandon the old syllabus based on the mathematical side of statistics and concentrate on the correct selection of procedures and tests, on their biological interpretation and on the use of reliable and friendly freeware. In most cases, a well designed one-semester course should be enough for them. It is a matter of common sense: their time will be better spent understanding and protecting tropical ecosystems than trying to learn the mathematical foundations of statistics.

References

Camacho, J. (ed.). 1997. Biometrical education: problems, experiences and solutions. Universidad Nacional and Centro Agrícola Tropical de Investigación y Enseñanza, Heredia, Costa Rica. [ Links ]

Fielding, W.J. & F.B. Lauckner. 1992. Biometric training in developing countries: Caribbean experiences. Statistician 41: 105-111. [ Links ]

Garfield, J. & D. Ben-Zvi. 2007. How students learn statistics revisited: a current review of research on teaching and learning statistics. Intern. Stat. Rev. 75: 372-396. [ Links ]

James, F.C. & C.E. McCulloch. 1990. Multivariate analysis in ecology and systematics: Panacea or Pandora’s box? Annu. Rev. Eciol. Syst. 21: 1 [ Links ]

Monge-Nájera, J. 2002. How to be a tropical scientist (50

Metz, A.M. 2008. Teaching statistics in biology: using inquiry-based learning to strengthen understanding of statistical analysis in biology laboratory courses. CBE—Life Sci. Educ. 7: 317-326. [ Links ]

Camacho, J. (ed.). 1997. Biometrical education: problems, experiences and solutions. Universidad Nacional and Centro Agrícola Tropical de Investigación y Enseñanza, Heredia, Costa Rica. [ Links ]

Fielding, W.J. & F.B. Lauckner. 1992. Biometric training in developing countries: Caribbean experiences. Statistician 41: 105-111. [ Links ]

Garfield, J. & D. Ben-Zvi. 2007. How students learn statistics revisited: a current review of research on teaching and learning statistics. Intern. Stat. Rev. 75: 372-396. [ Links ]

James, F.C. & C.E. McCulloch. 1990. Multivariate analysis in ecology and systematics: Panacea or Pandora’s box? Annu. Rev. Eciol. Syst. 21: 1 [ Links ]

Monge-Nájera, J. 2002. How to be a tropical scientist (50

^{th}anniversary editorial). Rev. Biol. Trop. 50: 19-23. [ Links ]Metz, A.M. 2008. Teaching statistics in biology: using inquiry-based learning to strengthen understanding of statistical analysis in biology laboratory courses. CBE—Life Sci. Educ. 7: 317-326. [ Links ]

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Correspondencia a: Natalia Loaiza Velázquez. Biología Tropical, Universidad de Costa Rica, 2060 San José, Costa Rica; natalia.loaiza@ucr.ac.cr

María Isabel González Lutz. Escuela de Estadística, Universidad de Costa Rica, 2060 San José, Costa Rica; mariaisabel.gonzalezlutz@ucr.ac.cr

Julián Monge-Nájera. Laboratorio de Ecología Urbana, UNED, 2050 San José, Costa Rica; Universidad de Costa Rica, 2060 San José, Costa Rica; julianmonge@gmail.com

María Isabel González Lutz. Escuela de Estadística, Universidad de Costa Rica, 2060 San José, Costa Rica; mariaisabel.gonzalezlutz@ucr.ac.cr

Julián Monge-Nájera. Laboratorio de Ecología Urbana, UNED, 2050 San José, Costa Rica; Universidad de Costa Rica, 2060 San José, Costa Rica; julianmonge@gmail.com

Received 13-IX-2010. Corrected 03-II-2011. Accepted 01-III-2011.