Basic Statistics

Whether you want to be a successful business owner, an economist, a psychologist, a medical doctor, a nurse, an environmental scientist, an engineer, or any other vocation (yep, even if you want to be a politician), it is likely that you will encounter data and statistics during the course of your studies and career. You may need to make decisions based on this data. You want to make sure that your decisions are correct, that the money is well-spent etc. For that, you need to understand how to analyse and interpret data.

During your studies and in your career, you may have a burning question about something, e.g., why some birds are only found in a certain patch of woods, why a type of product is a best-seller, which medicine formula is effective in combatting a disease, or why a certain demography of voters behaved in a certain way in an election. To answer these questions (and so many other questions), you would employ a quantitative research process, collect data, and use statistics. As part of this, you need to decide how you would collect data and how you draw the sample from your target population. You need to understand what type of analysis is the most suitable for your data, including describing data as well as formulating and testing hypotheses. Finally, you need to present your results in an effective manner.

For these reasons, the Learning Centre provides learning resources on basic statistics. These materials are divided into four modules:

Module A:
Data types and methods of sampling

Module B:
Descriptive statistics (including measures of central tendency and measures of spread) and normal distribution

Module C:
Inferential statistics (hypothesis testing, p-values and significance, choosing the right test)

Module D:
Presenting data

Module A helps you to understand the importance of data types and data collection methods. Once you have collected your data, Module B helps you to understand the “behaviours” of your data, e.g., whether they are normally distributed or not, and how to describe your data (descriptive statistics). In module C (inferential statistics), you will learn about hypothesis testing and what statistical significance is. In addition, module C covers the important process of choosing the correct statistical analysis for your context. Lastly, module D guides you in presenting your results in an effective manner.

A guide for R Studio Basics is available here.