This course is the second module of a series of independent modules in statistics applied to the life sciences. This second course is addressed to those who work with animal experiments, although this can be transposed into other fields. The examples are presented in real context with simulated data.

The aim of this course is to learn and apply statistical methodologies that can be used during many stages of the experimental process. The use of these techniques will assure that experiments are conducted in a logical and efficient way, which should result in reliable and reproducible decisions.

A range of studies design will be addressed in this course, including: block, factorial, nested, split-plot, crossover, and repeated measures. The randomization process, carried out at an early stage, will be described for each of the study designs presented. Alongside each design, statistical techniques will be presented to analyze the experimental data, such as:

·         analysis of variance (ANOVA),

·         analysis of covariance (ANCOVA),

·         nested models, and

·         fixed and random effects models (mixed effects models)

Understanding these methodologies can provide researchers with tools to help them reach valid conclusions, and refine the experimental process.

This short course provides an introduction to basic concepts of statistical methods and data analysis with R applied to life science. The course covers the following topics:

 • Introduction to R software

 • An overview of statistics. Data description: measurement scales, adequate graphic display

• Summarizing data: measures of central tendency and variability • Differentiation between population and sample. How to use a statistic to estimate a population’s parameter,

• Confidence interval and its interpretation

• Hypothesis testing: how to set up Null and Alternative hypotheses, understanding Type I and Type II errors

• Comparing two population means, proportions or variances: independent data versus paired data

• Identifying relationships between two variables: categorical variables (chi-square test of independence); quantitative variables (correlation and linear regression)

• Linear regression foundations: least squares method, inferences about the parameters

• Introduction to analysis of variance (ANOVA) methods