Description Most professions in the health sciences need to read and interpret statistics relating to individual health status, interpret health risks in communities, and engage in the evaluation of interventions, or impact of health policies or programs. Many public health practitioners are actively involved in surveillance, quantitative research and/or evaluation. This unit provides students with the fundamental skills they need to analyse and interpret results from quantitative data collections. Content includes descriptive statistics, undertaking comparisons between groups, quantifying associations between variables, and statistical power. The unit is highly applied with the main focus being on interpretation and appraisal of statistical results and conducting analyses using statistical software.
School Health Sciences
Discipline Health, Not Elsewhere Classified.
Student Contribution Band HECS Band 2 10cp
Level Postgraduate Coursework Level 7 subject
Restrictions
Students must be enrolled in a postgraduate program.
Assumed Knowledge
High school mathematics (arithmetic, formulas and algebra, reading graphs).
On successful completion of this subject, students should be able to:
Select appropriate descriptive statistics and graphs for different data types and produce these from a data set using statistical software.
Explain key concepts such as statistical inference, the central limit theorem, sampling distributions, probability distributions, confidence intervals, hypothesis tests, etc
List the key steps required to produce a confidence interval and to conduct a hypothesis test
Use statistical terms and symbols correctly when reviewing and interpreting statistical material
Compute statistical power or required sample size for a confidence interval or hypothesis test
Select appropriate statistical analyses to address a given research question and implement these analyses on a data set using statistical software
Interpret the output of statistical analyses clearly and correctly in a written report
Appraise the appropriateness of statistical results relative to all relevant considerations: including the research question, data types, shape of distribution, statistical power and other, procedure specific, assumptions.
1. Data and data types (categorical, ordinal, quantitative, etc)
2. Descriptive statistics (mean, standard deviation, median, quartiles, frequency, relative frequency) and graphs (bar chart, histogram, scatterplot, boxplots, run charts, etc)
3. Using statistical software
4. Statistical inference (populations, random samples and the probability relationship between them)
5. Probability distributions (binomial and normal), what they represent and how they are used
6. Confidence intervals as a method of statistical inference and the role of the central limit theorem
7. Hypothesis tests as a method of statistical inference, the 5 steps in hypothesis testing
8. t-procedures for statistical inference on means and mean differences
9. Chi-square procedures for statistical inference on associations between categorical variables
10. Statistical power and sample size in relation to one sample and difference between two sample confidence intervals and hypothesis tests only
11. Correlation and multiple regression models and associated confidence intervals and hypothesis tests
12. Nonparametric alternatives to t-procedures
Assessment
The following table summarises the standard assessment tasks for this subject. Please note this is a guide only. Assessment tasks are regularly updated, where there is a difference your Learning Guide takes precedence.
Item
Length
Percent
Threshold
Individual/Group Task
Short Answer
2 hours
30
N
Individual
Short Answer
2 hours
30
N
Individual
Report
2 hours
40
N
Individual
Prescribed Texts
Sullivan LM. Essentials of Biostatistics in Public Health (2nd ed). Jones & Bartlett, Sudbury, MA. 2012. ISBN-13: 978-0-7637-9531-3, ISBN-10: 0-7637-9531-3, OR
Sullivan, L. M. (2017). Essentials of biostatistics in public health (3rd ed). Jones & Bartlett, Sudbury. ISBN: 9781284108194
