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examBoard: AQA
examType: GCSE
lessonTitle: Null Hypothesis
When psychologists conduct research, they need a way to test if their ideas are supported by evidence. This is where the null hypothesis comes in - it's a crucial part of the scientific method that helps researchers make sense of their findings.
Key Definitions:
The null hypothesis serves as a starting point for research. It assumes that any patterns or relationships we observe in our data happened by chance. By trying to disprove the null hypothesis, researchers can build evidence for their alternative hypothesis.
We use null hypotheses because it's easier to disprove something than to prove it with absolute certainty. This approach helps make research more rigorous and reduces the chance of claiming false relationships.
Writing a good null hypothesis is straightforward once you understand the basic principles. The null hypothesis always predicts no effect, no difference, or no relationship.
A well-written null hypothesis should:
"There is no relationship between [variable 1] and [variable 2]."
"There is no difference in [dependent variable] between [group 1] and [group 2]."
H₀: There is no difference in recall scores between participants who study before sleep and those who study in the morning.
H₀: There is no relationship between hours spent studying and exam performance.
H₀: Violent video games have no effect on aggressive behaviour in teenagers.
Understanding the difference between null and alternative hypotheses is essential for designing good research.
States: No effect, no difference, no relationship
Example: There is no relationship between sleep quality and exam performance.
Purpose: To be tested and potentially rejected
States: There is an effect, difference, or relationship
Example: Better sleep quality is associated with higher exam performance.
Purpose: What the researcher actually expects to find
Even experienced researchers can make mistakes when formulating hypotheses. Here are some common errors to avoid:
In 1935, John Ridley Stroop conducted a famous experiment on attention and interference. Participants had to name the ink colour of words, where the words themselves were colour names (e.g., the word "RED" printed in blue ink).
Null Hypothesis: There is no difference in reaction time when naming ink colours of congruent words (e.g., "RED" in red ink) compared to incongruent words (e.g., "RED" in blue ink).
Result: The null hypothesis was rejected. Participants took significantly longer to name ink colours when the word and ink colour were incongruent, demonstrating what we now call the Stroop Effect.
The null hypothesis is central to how we analyse research data and draw conclusions.
When analysing data, researchers calculate a p-value, which tells us the probability of getting our results if the null hypothesis were true. By convention:
Notice that we never "prove" or "accept" the null hypothesis - we can only reject it or fail to reject it based on our evidence.
Rejecting a null hypothesis that is actually true (a "false positive").
Example: Concluding that a therapy works when it actually doesn't.
Failing to reject a null hypothesis that is actually false (a "false negative").
Example: Concluding that a therapy doesn't work when it actually does.
Follow these steps to write clear, testable null hypotheses for your psychology research:
Researchers wanted to investigate whether being mentally tired affects people's moral decisions.
Research Question: Does cognitive load affect moral decision-making?
Null Hypothesis: There is no difference in moral decision-making scores between participants under high cognitive load and participants under low cognitive load.
Alternative Hypothesis: There is a difference in moral decision-making scores between participants under high cognitive load and participants under low cognitive load.
Method: Participants were randomly assigned to either memorise a 2-digit number (low load) or a 7-digit number (high load) while making moral judgments about various scenarios.
Result: The null hypothesis was rejected (p < 0.01), showing that cognitive load does affect moral decision-making.
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