R for Statistical Significance Tests

Overview

What you will study

The R for Statistical Significance Tests course offered by University of Oxford assumes prior knowledge of how to use R and RStudio. It contains explanation and implementation of several tests such as:

• t-test to determine if there is a significant difference between the means of two groups, which may be related in certain features, or to answer the question: is the mean of a vector different from a given value? This includes variations of the t-test.

• Kolmogorov-Smirnov test to statistically test the distribution of a variable.

• A/B test to establish which of two treatments, products, procedures, or the like is superior.

• Permutation test to compare an observed statistic to a resampled distribution and determine whether an observed difference between samples might occur by chance.

• ANOVA to test whether groupings in the data can be meaningful ways to understand the structure of the data.

• Chi-Squared test to test differences across a contingency table.

• This training covers various theoretical and practical aspects of several hypothesis and statistical significance tests. You will learn what a test does, when to use it, how to use it and how to interpret its results.

By the end of the day you will have access to all course material (e.g. slides, code examples and so on).