Class Details
Statistics Course for Non-Statisticians
Course summary
Statistics offers tools that can assist anyone who is working with data. Statistics is very diverse and applicable to a wide range of disciplines in the government, institutional, NGO and commercial sectors, including health, finance, education, social sciences and other areas of scientific endeavour where gathering, organising, analysing, interpreting, and communicating quantitative (numerical) data is required.
Using minimal mathematics, this one-day course will explore common descriptive and inferential statistics. This includes statistical terminology and concepts such as statistical significance (p values), practical significance (effect sizes) and confidence intervals. Using different types of data, we will identify the most appropriate statistical test, conduct it, and then interpret the results. Statistical tests will include t-tests, Analysis of Variance (ANOVA), linear regression and chi square tests. Considerations for selecting a sample size will also be explored.
Aims
This course aims to give you the skills to understand essential statistical terminology and concepts, and how to interpret those statistics in reports. It also aims to equip you with the basics for conducting your own statistical analyses.
Learning outcomes
By the end of this course, you should be able to:
- identify the type of data you have or the type of data reported on in a research report
- ask and answer a researchable question or hypothesis
- check the underlying assumptions of your data in order to choose an appropriate statistical test
- determine a suitable sample size obtain descriptive statistics and conduct inferential statistical tests using free, downloadable statistical software
- interpret a range of common descriptive and inferential statistics.
Content
Prelude to statistical testing
- The three types of data:
- continuous or interval
- ordinal
- nominal
- Considerations for asking a researchable question or stating an hypothesis
- Measures of central tendency and dispersion
- Data distributions and ways of presenting them
- Parametric vs nonparametric statistics
- Statistical terminology such as p values, effect size, confidence intervals, Type 1 and Type II errors
- Sample size calculations
Statistical testing
- Choosing a statistical test based on the research question or hypothesis
- Conducting and interpreting parametric statistical tests, including t-tests, ANOVA and linear regression
- Conducting and interpreting nonparametric statistical tests, especially Chi-Square tests
Who this course is for
Suitable for people from all sectors who need to review research reports or conduct their own research. Data sets provided in this workshop for educational purposes may not necessarily fit with the research area of all participants; however, skills learned here are transferable to other types of data. This course can be viewed as either an introduction to some of the more commonly used statistics and methods of analysis, or as a refresher course.
Prerequisites
Although this course is designed to be as non-mathematical as possible, some basic numerical aptitude is required.
Delivery style
This course consists of mini-lectures followed by practical exercises.
Delivery modes
- Face-to-face, presenter-taught training in a computer lab
- Online training via the platform Zoom
Materials
Course materials are distributed electronically using Dropbox. You will be provided with a link to all course materials, including Excel data sets for use during the course.
What you need to do before the course
To participate in this course, you will need the following free software (Windows and Mac versions available):
- Jamovi
- G*Power
You will also need Microsoft Excel installed on your device.
Face-to-face classes
Classes run in a computer lab. You do not need to bring your own device or account login details.
Online classes
For the best experience, we recommend using a computer with a large monitor or dual screens/devices. Small laptop screens can make it difficult to follow the facilitator’s display while working in the software.
Sessions
Location
Face-to-face (CCE Building, Sydney)
Public transport
As bus and train timetables regularly change, we suggest you visit the Transport for NSW trip planner before you set out.
The nearest train stations are Newtown Station and Macdonaldtown Station. Macdonaldtown Station is closer by distance than Newtown Station, however trains do not stop as frequently as at Newtown Station.
Other information
Parking
Nearby parking is available at Wilson Parking - RPA Medical Centre Car Park (entry via Elizabeth Street off Carillon Avenue).
A Flexi Saver Rate is available but must be booked in advance online and includes the booking fee. Bookings can be made via the Wilson website or by downloading the Wilson Parking App. Otherwise, hourly parking rates will apply, as advertised on the Wilson Parking website.
Meet the facilitators
Andrew Taylor
Andrew has delivered workshops on data strategy, program evaluation and AI applications in social science. His expertise includes machine learning, multimodal AI pipelines and advanced statistical analysis. He has previously taught on topics including restorative justice program evaluation and data-driven decision making.
Andrew worked as a Senior Research Associate at the Vera Institute of Justice, where he led AI-driven data solutions and ETL pipeline development across cloud platforms. He has managed research teams and analytics projects for John Jay College of Criminal Justice and government agencies, with a focus on equity, diversion programs and public health.
Andrew holds a Master of Public Administration from the University of Washington and is a Pi Alpha Alpha Honours inductee. He has completed advanced training in AI and machine learning, including LangChain systems, LLMOps, and prompt engineering, alongside certifications in Principles of Machine Learning and AI strategies from UC Berkeley and Deeplearning.ai.
