Basic statistics

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Summary of the module

Statistics is a central tool in science, but it tends to be the pet peeve of students. We need statistics to summarize and describe quantitative data, but also to assess the confidence we have in these data and to understand the relationships between phenomena. This module gives the fundamentals of statistics through a practical approach, rather than a mathematical approach that would focus on scary equations. Study design and sampling methods are covered, with an emphasis on data analysis and interpretation. This 5-day module covers basic statistics (basic principles, descriptive statistics, simple tests) and their application in R. A format without the applications in R is possible over 2 days.

Detailed content of the sessions

Principle of statistics: generalize by measuring the risk of error
Descriptive statistics, usual graphs
How to choose a test
How does a test work, what is p-value?
Basic tests (t-test, F-test, proportion tests, principle of nonparametric tests).

Specific objectives:

At the end of the module, participants are able to:
Formulate a research question;
Identify response and explanatory variables;
Identify the type of variable and the tools adapted to the study of each type;
Import and clean data in R;
Make the usual graphics in R;
Do basic tests in R;
Interpret and write the results of analyses.

Lead trainers

References

Prerequisites / Target audience:

Master degree.

Evaluation

From a dataset, formulate the research question, identify the answer variable and the appropriate analyses, do the analyses in R and write the result. It is the R script that is retrieved for evaluation.