BMSC100 - Introductory Biostatistics

Year

Credit points

Campus offering

Prerequisites

Teaching organisation

Unit rationale, description and aim

The ability to describe, manage, analyse and interpret data is crucial to the practice of science. Therefore, it is important for biomedical scientists to understand and competently use statistical techniques and to be able to draw valid conclusions from statistical analyses. In this unit, students will learn to describe, manage, summarise, and present data. They will be introduced to the most commonly-encountered types of variables, learn how to recognise and apply appropriate statistical tests, and learn to interpret and effectively communicate the results. Students will gain foundational knowledge of statistical models used in biomedical science, and develop skills to implement such models by hand and using statistical software. The aim of this unit is to help students to develop the understanding and skills needed to recognise and apply appropriate statistical tools. 

Learning outcomes

To successfully complete this unit you will be able to demonstrate you have achieved the learning outcomes (LO) detailed in the below table.

Each outcome is informed by a number of graduate capabilities (GC) to ensure your work in this, and every unit, is part of a larger goal of graduating from ACU with the attributes of insight, empathy, imagination and impact.

Explore the graduate capabilities.

On successful completion of this unit, students should be able to:

LO1 - Demonstrate an ability to graphically and statistically characterise datasets in terms of distribution, skew, spread and central tendency (GA4, GA5, GA10)

LO2 - Select and describe the appropriate predictive linear models for statistical analysis of a range of commonly encountered types of data (GA5, GA9)

LO3 - Demonstrate an ability to use data-management techniques and statistical software to manage, summarise and analyse data (GA4, GA5, GA10)

LO4 - Interpret and draw valid conclusions from statistical output and communicate the results in standard ways (GA4, GA5, GA9, GA10)

Graduate attributes

GA4 - think critically and reflectively 

GA5 - demonstrate values, knowledge, skills and attitudes appropriate to the discipline and/or profession 

GA9 - demonstrate effective communication in oral and written English language and visual media 

GA10 - utilise information and communication and other relevant technologies effectively.

Content

Topics will include: 

• Exploring data with graphs
• Summarising data using descriptive statistics 
• Sampling bias
• Using software for statistical analyses 
• Hypothesis testing 
• Comparing averages
• Correlation and linear regression 
• Parametric versus non-parametric models
• Measures of association

Learning and teaching strategy and rationale

Biomedical scientists need to be able to use, interpret and draw conclusions from statistical analyses and communicate their findings to others. Acquiring these skills requires active involvement in solving statistical problems and applying the knowledge gained. Therefore, classes focus on practice in applying statistical concepts and techniques, and learning to interpret and communicate statistical output in a standardised way.  The unit is structured to progressively build knowledge week by week. Major components of assessment are integrated within classes, allowing students to actively engage in the process of collecting, managing, summarising, presenting and statistically analysing data. Lecture materials covering basic concepts are available throughout the semester and are supplemented by interactive and applied examples. Small-group sessions provide an opportunity for greater individual attention as developing skills and knowledge are applied to problems grounded in real data.   

Assessment strategy and rationale

Research demonstrates that embedding activities linked to assessments into each class helps develop understanding of statistical concepts and their applications. Therefore, a key component of classes each week is students applying statistical concepts covered in class to data they are using as part of their assessment tasks. The progressive learning approach, in which students gradually build understanding, is supported by a series of quizzes which target key concepts and allow students to assess their own progress. The second assessment uses real data that students collect in class and with which they follow a process which mimics a real-world situation of collecting, managing, describing and presenting the data, through statistical analysis, to communication of results. The end-of-semester examination allows students to demonstrate their understanding of statistical concepts, and their ability to interpret and communicate statistical results. 

Overview of assessments

Representative texts and references

Baldi, B., & Moore, D. S. (2018). The practice of statistics in the life sciences (4th ed.). W.H. Freeman, Macmillan Learning. 

Barton, B., & Peat, J. K. (2014). Medical statistics : a guide to SPSS, data analysis, and critical appraisal (2nd ed.). Wiley-Blackwell. 

Bland, M. (2015). An introduction to medical statistics (4th ed.). Oxford University Press. 

Dytham, C. (2011). Choosing and using statistics : a biologist’s guide (3rd ed.). Wiley-Blackwell.

Field, A. P. (2018). Discovering statistics using IBM SPSS statistics (5th ed.). SAGE Publications. 

Kirkwood, B. R., & Sterne, J. A. C. (2003). Essential medical statistics (2nd ed.). Blackwell Pub. 

Moore, D. S. Notz, W., & Fligner, M. A.(2021). The basic practice of statistics (9th ed.). Macmillan Education. 

Portney, L. G. (2020). Foundations of clinical research: Applications to practice (4th ed.). F. A. Davis