Understanding Different Types of Data
Data comes in two main flavours that you need to recognise straight away. Qualitative data involves categories like favourite subjects or hair colour, whilst quantitative data deals with actual numbers you can measure or count.
Within quantitative data, there's an important split. Discrete data comes from counting things (like the number of students in your class), while continuous data comes from measuring (like your height or weight). This distinction matters because it affects how you'll analyse your results later.
Bivariate data looks at pairs of related information - think about how study time might relate to exam scores. Multivariate data gets more complex, examining three or more related factors simultaneously.
Key Insight: Understanding data types from the start prevents major mistakes in your analysis later on.
Primary vs Secondary Data Sources
Primary data is information you collect yourself through surveys, experiments, or observations. It's brilliant because you control exactly how it's gathered, making it perfect for answering your specific questions. However, it's time-consuming and can be expensive to collect.
Secondary data comes from someone else's research - government statistics, published studies, or online databases. It's quick, cheap, and often comes from reliable organisations with more resources than you have. The downside? You don't know exactly how they collected it, and it might not answer your precise question.
Most successful investigations combine both types cleverly. Use secondary data for background research, then collect targeted primary data to fill the gaps.
