Hypotheses (Edexcel GCSE Statistics): Revision Notes
Hypotheses
What is a hypothesis?
A hypothesis is a clear, precise statement about something that you can measure and test through data collection and analysis. Think of it as an educated guess that you can either prove right or wrong by gathering evidence. It might turn out to be true, or it might not - that's perfectly fine! The important thing is that you can collect relevant data and examine it to see whether your hypothesis is supported or contradicted.
The key to a good hypothesis is that it must be testable. This means you need to be able to gather real data that will either support or contradict your statement.
Requirements for a good hypothesis
For your hypothesis to be useful in a statistical investigation, it must meet two essential criteria:
Be specific
Your hypothesis needs to be exact and well-defined. Vague terms like "young people" or "older people" won't work because they can mean different things to different people. Instead, you need to define exactly what you mean by using specific measurements or categories.
Example: Comparing Gym Usage
Poor example: "Young people spend more time at the gym than old people"
- The problem here is that "young" and "old" are not clearly defined. What age counts as young? What age counts as old?
Better example: "People under 30 spend more time at the gym than people over 50"
- This is much clearer because it specifies exact age ranges.
Be measurable
Your hypothesis must involve something that can actually be measured and recorded as data. You need to be able to collect numerical information or categorise responses in a systematic way.
Good example: "As people get older they need more sleep"
- This works because you can measure age in years and amount of sleep in hours, then group the data appropriately for analysis.
Worked examples breakdown
Example 1: Gym usage hypothesis
Worked Example: Improving Matt's Hypothesis
Matt's original hypothesis: "Young people spend more time at the gym than old people"
Why this doesn't work: The statement lacks precision because "young" and "old" aren't clearly defined. Without specific age ranges, different people might interpret these terms differently, making it impossible to collect consistent data.
Improved version: "People under 30 spend more time at the gym than people over 50"
Why this works better:
- It defines specific age groups (under 30 and over 50)
- It's measurable (you can record ages and gym time in hours)
- It allows for clear data collection and comparison
Example 2: Mobile phone usage hypothesis
Worked Example: Rosa's Mobile Phone Study
Rosa's hypothesis: "Female students spend more time on their mobile phones than male students"
Why this works:
- It's specific about the groups being compared (male vs female students)
- It focuses on something measurable (time spent on phones)
Data collection considerations: To test this hypothesis, Rosa would need to collect primary data showing the amount of time spent per day on mobile phones (measured to the nearest minute) for both male and female students. This creates clear, comparable data sets.
Planning your data collection
When writing your hypothesis, you also need to consider how you'll actually gather the evidence to test it.
Key Planning Considerations:
Age groups or categories: If your hypothesis involves different groups of people, define these clearly. For example:
- Less than 30 years
- 30 to 50 years
- Over 50 years
Time periods: Be specific about timeframes for data collection:
- How many hours per week?
- Daily usage?
- Monthly patterns?
Measurement units: Specify exactly how you'll measure your variables:
- Hours and minutes for time
- Numbers of items
- Specific scales or categories
Common exam mistakes to avoid
Critical Mistakes That Will Lose You Marks:
- Using vague terms: Avoid words like "young," "old," "many," or "few" without defining them precisely
- Making statements that can't be measured: Ensure everything in your hypothesis can be turned into numerical data
- Being too broad: Keep your hypothesis focused on one specific relationship you can test
- Forgetting about data collection: Always consider whether you can realistically collect the data needed to test your hypothesis
Exam technique tips
- Always check that your hypothesis contains specific, measurable terms
- Think about whether you could actually collect data to test your statement
- Consider what type of data (primary or secondary) you would need
- Make sure your statement is clear enough that someone else could understand exactly what you mean
Key Points to Remember:
- A hypothesis is a testable statement that can be supported or contradicted by data
- It must be specific - define all terms clearly with exact measurements or categories
- It must be measurable - you need to be able to collect numerical data or systematic observations
- Think about your data collection method when writing your hypothesis - can you actually gather the evidence you need?
- Avoid vague language and ensure someone else could understand exactly what you're testing