At the end of this course, students will be able to apply principles of biostatistics in research projects in parasitology. A dialogue with statisticians and epidemiologists is encouraged when a problem requires more advanced mathematical and epidemiological details. After the course students should be able to: 1. To demonstrate a basic understanding of the importance of statistics in research projects in parasitology. 2. To know some sampling methods, given attention to the randomness. 3. To calculate, interpret and summarise the results of the descriptive statistics and exploratory data analysis for the purpose of scientific publications 4. To perform statistical hypothesis tests (parametric and nonparametric) and confidence intervals, providing a critical interpretation of results and given a particular attention to the assumptions of each test.
Weekly - 2,5
Total - 28
• Morrison, D.A. (2002) How to improve statistical analysis in parasitology research publications. Int J Parasitol., 32(8), 106570. • Armstrong, R.A., Hilton, A.C. (2010) Statistical Analysis in Microbiology: StatNotes. WileyBlackwell. • CallegariJacques, S. (2003) Bioestatística – Princípios e Aplicações. Artmed Editora SA. • Daniel, W.W. (2004) Biostatistics: a foundation for analysis in the health sciences. John Wiley and Sons, 8th Ed. • Sheskin, D. J. (2007) Handbook of Parametric and Nonparametric Statistical Procedures. Chapman and Hall/CRC. 4th Ed.
The total contact hours (30 hrs.) will be distributed by 9 theoretical and practical (18 hrs.), 5 tutorial sessions (10 hrs.) and assessment (2 hrs.) . In the practical sessions will be used statistical package (e.g. SPSS, EPITools and others) and other online platforms (e.g. Moodle).
The final assessment will be by written exam. The exam includes different type of questions (e.g. multiple choice, true/false and essay questions).
I. The role of the biostatistics in parasitology. II. The importance of random sampling in research. III. Definition and classifying variables. Cautions in data collection and data entry. IV. Descriptive statistics and exploratory data analysis. V. Statistical inference: parameters, statistics and sampling distributions; estimation of parameters, hypothesis testing. Concepts of parametric tests vs nonparametric tests. Confidence intervals for mean values and proportions. Alternative methods for prevalence, sensitivity, and specificity of the diagnostic tests. Sample size calculation, using confidence intervals and other situations. VI. Comparisons of populations though independent sample: assumptions of parametric test: Kolmogorov-Smirnov and Shapiro-Wilk tests and Levene test; T-Test vs Mann-Whitney; ANOVA vs Kruskall-Wallis test. Multiple comparisons. Chi-Square tests. VII. Correlation and linear regression. Introduction to logistic regression.
Programs where the course is taught: