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An ability to interpret statistics and data is important for many professions for making decisions based on valid interpretations of statistics and data. This course caters for demand across all sectors and disciplines such as finance, health and the social sciences, and is intended for professionals required to collate, interpret and analyse data.

This statistics course enables you to acquire skills in understanding statistical terminology and interpreting data. It also provides an introduction to testing for statistical significance, when dealing with sample data. We will examine different types of data and develop evidence-based judgements. In so doing, we will apply tests of statistical significance, interpret p-values, determine confidence intervals and consider Bayesian methodology.

Objectives

The course objectives include:

• increasing your use of statistical terminology
• building your familiarity with sample design considerations when choosing a statistical testing procedure
• determining sample sizes for achieving required levels of statistical confidence
• generating hypotheses for testing
• selecting between parametric versus non-parametric statistical testing
• how to conduct statistical testing and interpret findings
• how to interpret correlation and regression.

Outcomes

By the end of this course, you should be able to:

• use statistical terminology for designing research, interpreting data and reporting conclusions
• select a sample design for answering a research question
• represent data for making meaningful interpretations
• select between a parametric and non-parametric test based on underlying assumptions
• test a hypothesis using statistics, drawing conclusions and evidence-based decisions
• interpret statistical findings using confidence intervals and by applying Bayesian thinking.

Content

Introduction to statistical terminology

Understanding terms such as statistics, probability, sample frames, sample designs, statistical confidence, p-values, type 1 and type 2 errors, parametric versus non-parametric testing, data distributions, measures of central tendency, standard deviation.

Sampling considerations as a pre-requisite to statistical testing

Use of probability samples versus convenience/ non-probability sampling. Implications of sample design in choice of statistical testing.

Ways of processing data

Use of Box and Whisker plots, application of central limit theorem, processing data from different underlying data distributions.

Use of parametric testing

Z-tests and t-tests using the standard normal distribution. Interpreting statistical tables. Understanding the implications of type 1 and type 2 errors.

Hypothesis testing with test statistics

Formulating the hypothesis, selecting an appropriate test, choosing level of significance, determining critical value or p-value, accepting/ rejecting the null hypothesis.

Analysis of variance, correlation and regression

Testing multiple samples, an introduction to multivariate analysis.

Use of non-parametric testing and Bayesian statistics

When and how to use a chi-square test, questions for answering with Bayesian statistics.

Intended audience

Suitable for people from all sectors – commercial, government, and education and not-for-profit. You are likely to be reviewing data sets or interpreting research reports and are seeking to do so with increased proficiency. This course is suitable for those without formal training in statistics; or looking for an introduction to statistics before seeking out more formal learning; or are looking for a refresher program.

Prerequisites

This course includes calculations using statistical formulae. Although no prior statistical knowledge is required, this course is unsuitable for people with very low numerical aptitude.

Delivery modes

• Face-to-face, presenter-taught training in a computer lab
• Online training via the platform Zoom

Delivery style

Coursework consists of mini-lectures, followed by practical exercises. These exercises are completed individually, in pairs, small groups and amongst the entire class, so as to build both your theoretical knowledge and practical skills.

Face-to-face classes

These classes run in a computer lab and you do not need to bring your own device.

Online classes

You will need your own device.

Materials

Course materials are distributed electronically using Dropbox.