In statistics, data can be categorised as either categorical (qualitative) or numerical (quantitative).
Why is it important?
Categorising data helps organise and interpret. Categorising your data is the first step to analysing it.
How to categorise your data
Follow the flow chat below.
Recommended resources:
Brehm, K. (2021). Statistics - 1.2 Classifying Data.
A graph for data is a visual representation of numerical or categorical data that helps in identifying patterns, trends, and relationships. Different types of graphs are used depending on the type of data and the purpose of the analysis.
Why is it important?
Selecting the right graph for your data is crucial for accurately conveying insights. Selecting an appropriate graph can also help you to inform what summary statistic is best for your data.
How to select a graph
Follow the flow chat below.
Recommended resources:
Communicating Research Findings Guide
Science Ready. (2024). Choosing the right graph in science.
Summary statistics are numerical measures that provide a concise overview of a dataset.
Why is it important?
Selecting the right summary statistic for your data ensures accurate representation of the data's central tendency, dispersion, or distribution. The right statistic helps draw meaningful conclusions and make informed decisions.
How to select a summary statistic
Categorical data will always use a percentage. For numerical data, create an appropriate graph, and determine the graph's symmetry.
Recommended resources:
Dr Nic. (2015). Summary statistics.