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The current and official versions of the course specifications are available on the web at https://www.usq.edu.au/course/specification/current.
Please consult the web for updates that may occur during the year.

STA2100 Evaluating Information

Semester 2, 2022 Online
Units : 1
Faculty or Section : Faculty of Health, Engineering and Sciences
School or Department : School of Mathematics, Physics & Computing
Grading basis : Graded
Course fee schedule : https://www.unisq.edu.au/current-students/administration/fees/fee-schedules


Examiner: Benjamin Dexter


Enrolment is not permitted in STA2100 if STA3100 has been previously completed.


An understanding of our society relies on the collection, analysis, interpretation, and dissemination of information about the people within our society. This course introduces basic concepts relevant to the effective collection, analysis, and interpretation of quantitative information from individuals and groups of individuals. An understanding of these concepts is essential to students in behavioural sciences, health sciences, educational studies, sociology, humanities, political science, business and management studies, legal studies, journalism and any discipline involving the initiation or critical appraisal of studies of social phenomena. Active participants should obtain enough knowledge to understand and critically analyse reports of many social science studies and develop sufficient practical skills to interpret information produced by a statistical software package. This course provides the foundations for application and further development in a range of programs.

Students are introduced to basic concepts and tools commonly involved in collecting, managing, summarizing, analysing, interpreting, and presenting quantitative data. No prior statistical or mathematical knowledge is assumed. Methods of descriptive and inferential statistics are introduced. Issues related to causation and confounding; the nature of variability, the reliability of summary statistics, the limitations and assumptions underpinning statistical techniques; the appropriate use of language in interpreting an analysis; and the use of computer output in understanding data summary and analysis are explored. The emphasis is on the concepts, interpretations, and applications of statistics as used in the analysis of data, rather than on mathematical or computational aspects. The use of case studies is emphasised and writing of reports facilitated.

Course learning outcomes

On completion of this course, students will be able to:

  1. Explain relationships and trends in data and distinguish between different methods of data collection.
  2. Critically evaluate information presented in academic literature and the media.
  3. Communicate the interpretation of statistical information.
  4. Independently develop and conduct a statistical study and appropriately report results.


Description Weighting(%)
1. Introduction to research data:
benefits and risks of using statistics, making sense of media reports, defining what is being measured.
2. Sampling, observational studies, surveys and experiments:
Research strategies, sampling methods, difficulties and disasters in sampling. Designing observational studies, difficulties and disasters in observational studies. Designing experiments, difficulties and disasters in experimental studies. Principles of control, random assignment, replication; single and double blinding.
3. Measurement and data description:
Variables, values and labels; data entry, checking and formatting; codebooks. Levels of measurement; nominal, ordinal, interval; continuous and discrete.
4. Data display and description:
Data distributions: bar graphs, stemplots, histograms, boxplots. Measures of central tendency; means, medians, percentiles, ranks. Measure of spread: standard deviations, range, IQR. z-scores; relevance, applications. Skewness and outliers. The normal distribution; proportions and scores.
5. Relationships between measurement variables: statistical relationships, scatterplots, correlation, causation, lurking variables, internal and external validity. 15.00
6. Relationships between categorical variables: Contingency tables, frequencies, percentages, proportions, probabilities. Conditioning: explanatory (independent) and response (dependent) variables, independence of variables, assessing statistical significance, chi-square test of independence. 10.00
7. Association and significance:
Measures of association; expected and observed frequencies; the chi-square test of independence. Logic of hypothesis testing; null and alternative hypotheses; Type I and II errors, power; P-values and their interpretation.
8. Making judgments from surveys and experiments:
Populations and samples. Interpretation of confidence intervals. Hypothesis testing about proportions and means. Impact of sample size; statistical and practical significance.

Text and materials required to be purchased or accessed

Utts, J.M 2015, Seeing Through Statistics, 4th edn, Cengage Learning.
All additional study material will be provided on the course StudyDesk.

Student workload expectations

To do well in this subject, students are expected to commit approximately 10 hours per week including class contact hours, independent study, and all assessment tasks. If you are undertaking additional activities, which may include placements and residential schools, the weekly workload hours may vary.

Assessment details

Approach Type Description Group
Weighting (%) Course learning outcomes
Assignments Written Critique (written) No 20 2
Assignments Written Planning document No 20 1,3,4
Assignments Written Report No 30 1,3,4
Assignments Written Quiz No 30 1,2,3,4
Date printed 10 February 2023