Statistical Methods in Research

Research | 2026

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Mode
Mode
Your studies will be on-campus, and may include some online delivery
On campus
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Area/Catalogue
RESE 5010
Course ID icon
Course ID
204861
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Campus
Adelaide City Campus East
Level of study
Level of study
Research
Unit value icon
Unit value
2
Course owner
Course owner
Graduate Research Training
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Course level
5
Work Integrated Learning course
Work Integrated Learning course
No
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Inbound study abroad and exchange
Inbound study abroad and exchange
The fee you pay will depend on the number and type of courses you study.
Yes
University-wide elective icon
University-wide elective course
Yes
Single course enrollment
Single course enrolment
Yes
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Note:
Course data is interim and subject to change

Course overview

This is an elective course in the Graduate Certificate in Research Skills and Master of Research programs intended to provide the student with an understanding of a range of key statistical methods used in the processing of research data. It will explain the use of statistical distributions and sampling as they relate to the conduct of data-based research across a wide range of disciplines.

The course will introduce different types of data and the effects of uncertainty and imprecision, principles underlying the use of statics to analyse data and make predications; common probability distributions, methods for testing statistical significance and hypothesis testing and analysis of variance along with commonly used tools used for analysis and visualisation. The ethical application of statistical methods within professional and national guidelines and frameworks will be addressed.

  • Understanding Data and Basic Probability Theory
  • Statistical Inference and Sampling Distributions
  • Regression Modelling and Data Analysis
  • Data Visualisation and Ethical Practice

Course learning outcomes

  • Use basic probability theory and statistical reasoning to explore patterns and variability in data.
  • Apply standard statistical methods, including hypothesis testing and linear regression, to analyse real-world data and interpret results.

Prerequisite(s)

N/A

Corequisite(s)

N/A

Antirequisite(s)

N/A

Degree list
The following degrees include this course