Statistics is all about collecting and understanding data to answer...
Statistics601 views·Updated Jun 21, 2026·2 pagesGCSE Statistics Chapter 1 Explained
1
of 2
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.
2
of 2
Experimental Design Methods
Laboratory experiments give you maximum control - you're like a director controlling every aspect of the scene. They're easy to repeat and you can eliminate outside factors that might mess up your results. The catch? People often behave differently in artificial lab settings than in real life.
Field experiments happen in natural environments where you still control the main variable you're testing, but can't control everything else. They show more realistic behaviour but are harder to repeat reliably.
Natural experiments involve observing what's already happening without changing anything yourself. You might study how a new school policy affects behaviour by comparing schools that have it with those that don't. These reflect real life perfectly but make it hard to prove what's actually causing the changes you observe.
Remember: The more control you have, the more reliable your results - but the less realistic they might be.
Sampling Techniques and Data Collection
A random sample gives every member of your population an equal chance of being chosen - this keeps things fair and unbiased. Number everyone in your population, then use a random number generator to pick participants.
Stratified sampling maintains the same proportions as your original population. If 60% of students are girls, then 60% of your sample should be girls too. Calculate this using: (number in category ÷ total population) × sample size.
When collecting data, questionnaires are quick and cheap for large samples, plus people answer more honestly. Interviews let you explain questions and get detailed responses, but they're expensive and your presence might influence answers. Choose based on your resources and what type of information you actually need.
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Statistics601 views·Updated Jun 21, 2026·2 pagesGCSE Statistics Chapter 1 Explained
Statistics is all about collecting and understanding data to answer real questions - from predicting exam results to testing new medicines. You'll learn how to gather reliable information and spot the difference between good data and dodgy data that could...
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of 2
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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.
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of 2Sign up to see the content. It's free!
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Experimental Design Methods
Laboratory experiments give you maximum control - you're like a director controlling every aspect of the scene. They're easy to repeat and you can eliminate outside factors that might mess up your results. The catch? People often behave differently in artificial lab settings than in real life.
Field experiments happen in natural environments where you still control the main variable you're testing, but can't control everything else. They show more realistic behaviour but are harder to repeat reliably.
Natural experiments involve observing what's already happening without changing anything yourself. You might study how a new school policy affects behaviour by comparing schools that have it with those that don't. These reflect real life perfectly but make it hard to prove what's actually causing the changes you observe.
Remember: The more control you have, the more reliable your results - but the less realistic they might be.
Sampling Techniques and Data Collection
A random sample gives every member of your population an equal chance of being chosen - this keeps things fair and unbiased. Number everyone in your population, then use a random number generator to pick participants.
Stratified sampling maintains the same proportions as your original population. If 60% of students are girls, then 60% of your sample should be girls too. Calculate this using: (number in category ÷ total population) × sample size.
When collecting data, questionnaires are quick and cheap for large samples, plus people answer more honestly. Interviews let you explain questions and get detailed responses, but they're expensive and your presence might influence answers. Choose based on your resources and what type of information you actually need.
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