Introduction to statistics

Welcome to the Introduction to statistics module. This module introduces key ideas that will help you understand statistics used in journal articles and other academic literature, and that you can use to analyse your own data. Note that if you are not planning to do either of these things, and instead only want to learn how to interpret a small number of common graphs and tables and calculate a few important values, you may find the Statistics and probability page of the Numeracy fundamentals module more suitable.

You can work through this module from beginning to end in the order provided, or you can move directly to specific pages or sections based on your needs or preferences. Additionally, if you are interested in learning how to carry out the statistical tests discussed in this module using the statistical software SPSS or Stata, you may also wish to complete the Introduction to SPSS or Introduction to Stata module. Note that all graphs and tables included in this module have been created using SPSS, except the tables in the Interpreting statistics page which have been taken from journal articles.

Before starting the module, you may also find it helpful to read the How to get confident with statistics post on The Research Whisperer blog. This post provides practical advice on building confidence in statistics and may help you make better use of this module.

Your feedback on this module is welcome at any time and can be provided through the feedback page. If you have questions about the module, please contact Library-UniSkills@curtin.edu.au

What you will learn

• What data and variables are, and how to identify different types, including categorical and continuous data and independent and dependent variables.
• How to interpret and critically evaluate statistics published in academic sources, including how to tell the difference between descriptive and inferential statistics and between statistical and practical significance.
• What descriptive statistics are, and how to display data and calculate descriptive statistics for one variable or two variables at a time, for both categorical and continuous data.
• What the normal distribution and sampling distributions are, and how to determine whether a variable is normally distributed or whether a sample size is large enough for the sampling distribution of the mean to be treated as a normal distribution.
• What inferential statistics are, including how to distinguish between estimation and hypothesis testing, how to calculate confidence intervals, and how to interpret confidence intervals and \(p\) values.
• How to compare the means of continuous variables using \(t\)-tests or one-way analysis of variance (ANOVA).
• How to test for relationships between pairs of variables using the chi-square test of independence or Pearson’s correlation.
• How to use linear and logistic regression to understand and explain relationships between variables.