Experimental Design

Code:

B4059

Acronym:

B4059

Keywords
Classification	Keyword
OFICIAL	Biology

Instance: 2021/2022 - 1S

Active?	Yes
Responsible unit:	Department of Biology
Course/CS Responsible:	Master in Biodiversity, Genetics and Evolution

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
M:BFBP	5	Plano de Estudos M:BFBP_2015_2016.	1	-	6	42	162
M:BGE	15	Official Study Plan	1	-	6	42	162
M:BGQ	8	Plano de Estudos do M:BGQA_2013-2014	1	-	6	42	162
M:GF	5	plano de estudos do Mestrado em Genética Forense a partir de 2013_2014.	1	-	6	42	162
M:RBA	3	Study plan since 2013/2014	1	-	6	42	162

Teaching language

English
Obs.: Opcionalmente em Português

Objectives

Most biologists contact with the problems of experimental design in a relaxed manner or not at all, refining their experiments empirically on a trial-error basis. During this course special attention will be paid to the logic underlying the whole process of experimental design, from drawing hypothesis, selection of statistical tests, planning and setup of experiments that unambiguously lead to interpretable results.

Learning outcomes and competences

With this course we intend to provide a solid background on experimental design for biology and, in particular, ecology.

Working method

Presencial

Pre-requirements (prior knowledge) and co-requirements (common knowledge)

Basic knowledge of statistics (mostly probability distribution and analysis). Basic knowledge of spreadshhets (e.g., excel), with special emphasis on the usage of mathematical functions.

Program

Logic behind scientific research: hypothesis testing and falsification. Induction vs deduction. Experiments and experimental design. Probabilities, statistics and sampling. Parameters and estimates. Populations and samples. Representativeness of samples. Types of sampling strategies. Precision and accuracy. Comparison of two samples: Student's t-test. Fundamentals of a statistical test: null and alternative hypothesis. Significance levels. Type I and II errors. Multiple comparisons. Analysis of Variance ((ANOVA). Factors in ANOVA. Single-factor ANOVA. Partition of variances in ANOVA. Linear model development. Assumptions of ANOVA. F-tests. Fixed or random factors? Multiple comparisons. Homoscedasticity tests. Replication and pseudoreplication. Data transformation. Two-way crossed ANOVA. Interactionsbetween factors. F-tests. Nested ANOVAs. Complex models with more than two factors. Cornfield-Tukey rules to build complex models. A priori multiple comparisons. Orthogonal and non-orthogonal tests. Power of tests. Power analysis. asymmetrical ANOVAs. Beyond BACI models for the study of environmental impacts.

Mandatory literature

Underwood, AJ; Experiments in ecology: their logical design and interpretation using analysis of variance, Cambridge University Press, 1997. ISBN: 978-0521553292

Teaching methods and learning activities

 

Theoretical-practical classes. The sequence of theoretical subjects will be accompanied by practical exercises, making use of simulated datasets. Students can (and should) participate by suggesting experiments dealing with original hypothesis for which datasets can be built using a simulator.

 

Software

http://www.fc.up.pt/pessoas/amsantos

Evaluation Type

Distributed evaluation without final exam

Assessment Components

designation	Weight (%)
Participação presencial	5,00
Trabalho escrito	95,00
Total:	100,00

Amount of time allocated to each course unit

designation	Time (hours)
Estudo autónomo	60,00
Frequência das aulas	42,00
Trabalho escrito	60,00
Total:	162,00

Eligibility for exams

Final report including: Clearly stated hypotheses on biological phenomena Adequate experimental design, including thoughts on non-statistical constraints related with data collection, handling, etc. Simulation of data, testing and interpretation of results Attendance to at least 75% of the classes

Calculation formula of final grade

During the course 1) Participation: 1 point In the report 1) Idea: 3 points 2) Complexity of the analysis: 4 points 3) Experimental design: 6 points 4) Interpretation: 4 points 5) Conclusions: 2 points The final classification will be obtained by summing the above mentioned values weighted by a percentage.

Classification improvement

New report. The participation component is maintained.