Course outline:

This course covers some of the basic and intermediate statistical methods and principles necessary for the analysis and interpretation of biological data.

Descriptive statistics (central tendency and variance); analysis of univariate and multivariate data; probability density and distribution functions; hypothesis testing and group comparison (parametric and nonparametric tests); Experimental design; Linear models and Generalized linear models; types of random data, Fourier transform and its application in image analysis, spectral and correlation analysis;  random error estimates in data measurements. P-values, multiple hypothesis testing and limitations;  power and sample size calculation.

Related readings:

Introduction to R: R for Data Science https://r4ds.had.co.nz/

Fundamentals of Biostatistics by Bernard Rosner, Harvard University

Articles collection from Point of Significance series from Nature Methods: https://www.nature.com/collections/qghhqm/pointsofsignificance

Course outcomes:

This course will help to understand the basics of general statistical methods used in hypothesis testing and how to choose the right statistical test suitable for the data type (and be aware of the limitations).

Through this course, we will also cover some basic R and statistical packages (implemented in R), which can be useful to apply on student’s own datasets in future. We will use MATLAB for random image analysis and correlation analysis.