Course Information

Finance. Boost your financial know-how.

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.

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:

1. continuous or interval
2. ordinal
3. 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

Intended audience

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

There are no 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 mode

• Face-to-face, presenter-taught training
• 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.

Before the course

Please download the following free software (Windows and Mac versions available):

1. Jamovi and user guide
2. G*Power and user guide