This lesson is being piloted (Beta version)

Statistical Comparisons using R: Glossary

Key Points

Introduction to Hypothesis Testing
  • Select appropriate and testable null and alternative hypotheses

  • Interpret p-values and statistical significance correctly

Preparation of Data
  • Open a data file in a text editor or RStudio file viewer

  • Use read.table or read.csv to import data

  • Review a dataframe using str and summary

  • Convert columns from factors to string using as.character

Relationship Between Continuous Variables
  • Distinguish a continuous variable

  • Review data using plot and ggplot

  • Test for normality of a dataset using shapiro.test

  • Calculate correlation coefficient using cor and cor.test

Categorical Variables
  • Convert dataframe columns to factors using as.factor

  • Draw barcharts using plot and ggplot

  • Select an appropriate statistical test for a categorical dataset

  • Analyse categorical data using chisq.test and fisher.test

Comparison Between Two Groups
  • Use hist and boxplots to review distribution of variables for a group

  • Summarise grouped data using the by command

  • Distinguish paired and non-paired samples

  • Correctly use the t.test and wilcox.test functions

Testing For More Than Two Groups
  • Identify situations needing multiple sample tests and choose the relevant test using the decision tree

  • Perform multi-group testing using aov and kruskal.test

  • Perform and interpret post hoc tests using TukeyHSD and dunn.test

  • Study interactions using interaction.plot and aov

  • Check model assumptions using plot

Multiple testing, summary, and final exercises
  • Understand the impact of multiple testing on p-values

  • Use p.adjust to correct multiple-testing p-values

Glossary

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