MATH1041 Statistics for Life and Social Science

MATH1041 is a Level I Mathematics course intended for students who do not propose to study any Mathematics beyond first year level. See the course overview below.

Units of credit: 6

Assumed knowledge: a level of knowledge equivalent to achieving a mark of at least 60 in HSC Mathematics is assumed; or a minimum level of 70 in HSC General Mathematics.

Exclusions: MATH2801, MATH2901, MATH2089, MATH2859, MATH2899, ECON1203, ECON2292

Cycle of offering: Yearly in Semesters 1 and 2.

Graduate attributes: The course will enhance your research, inquiry and analytical thinking abilities.

More information:

These recent outlines contain information about course objectives, assessment, course materials and the syllabus.  The Online Handbook entry contains up-to-date timetabling information.

The Online Handbook entry contains up-to-date timetabling information.

If you are currently enrolled in MATH1041, you can log into UNSW Moodle for this course.

For general advice, see advice on choosing first-year courses.

Course Overview

Statistics is about "making decisions in the face of uncertainty". Where should the next hospital be built in Sydney? It takes years to build a hospital, and we want to build it where it will be the most useful when it is built.... What is the best medication to treat a particular infection? ... Are two medications equally effective?

In statistics we attempt to gain or improve our understanding of the world using the information gleaned from sets of data. People in government study data in order to make informed planning decisions. Doctors, biologists, pharmacists, psychologists, geologists and many others need to understand the origins, trustworthiness and analysis of the data that appears in journal articles.

Statistics is the science of collecting, organising, analysing, and interpreting data. We introduce some of the ways of obtaining data, and what sort of questions someone might hope to answer using statistics. We look at various ways of summarising or graphing the data to get a good overview. We develop the ideas of statistical inference, that is:

  1. Testing hypotheses - for example "do we have enough evidence to conclude that a new product works better? or faster"; How much evidence would we need to make such a decision? "Does bread lose vitamin C when it is stored?"....
  2. Estimating important parameters with required levels of accuracy - how much faster does the new product work? How much vitamin C does bread lose when stored for one day? One week?...