Students begin by exploring the foundations of statistical investigations through the collection, classification, and organisation of data. They examine different types of variables, compare methods of data collection and sampling, investigate bias, and learn to organise data using tables and graphical representations. This unit establishes the importance of obtaining reliable and representative data before any statistical analysis can take place.
This unit focuses on describing and summarising datasets using numerical measures and graphical displays. Students calculate measures of centre and spread, investigate percentiles and standard scores, analyse the shape of distributions, and interpret box plots and histograms. They learn how descriptive statistics reveal important characteristics of data and support meaningful comparisons between different datasets.
Students investigate probability distributions as mathematical models for uncertainty and variation. They explore random variables, expected value, the binomial and normal distributions, and sampling distributions, including an introduction to the Central Limit Theorem. Through practical applications, students learn how probability models help predict outcomes and describe real-world phenomena.
In this unit, students learn how statisticians draw conclusions about populations using sample data. They construct and interpret confidence intervals, perform hypothesis tests, investigate statistical significance, examine Type I and Type II errors, and critically evaluate statistical investigations. Emphasis is placed on making evidence-based conclusions while recognising the limitations of statistical methods.
The final unit examines relationships between variables and the development of statistical models. Students investigate scatter plots, correlation, linear regression, multivariable relationships, and statistical modelling techniques before learning how to communicate statistical findings effectively. The course concludes by integrating concepts from throughout the course to analyse authentic datasets and solve real-world problems.
Subtopics
Scatter Plots and Correlation
Linear Regression
Multiple Variable Analysis
Statistical Modelling
Communicating Statistical Results
By the end of this course, students will be able to collect and organise data, analyse and interpret statistical information, apply probability distributions and inference techniques, evaluate statistical claims critically, and communicate evidence-based conclusions with confidence. They will understand how statistics supports decision making across science, medicine, engineering, economics, business, psychology, government, and countless other disciplines, providing a strong foundation for further study in mathematics, data science, artificial intelligence, and STEM fields.