Understanding Hypotheses in Psychology
In psychology research, a hypothesis is an educated guess or prediction about the relationship between variables that can be tested through research. It's like making a smart guess about what you think will happen in an experiment before you actually do it.
Key Definitions:
- Hypothesis: A testable prediction about the relationship between variables.
- Variable: Something that can change or vary in an experiment (like mood, memory, or behaviour).
- Operationalisation: Defining variables in terms of how they will be measured.
💡 Why Do We Need Hypotheses?
Hypotheses are essential because they:
- Give research a clear direction and purpose
- Allow predictions to be tested scientifically
- Help researchers stay focused on specific questions
- Make it possible to accept or reject ideas based on evidence
🔬 Where Do Hypotheses Come From?
Psychologists develop hypotheses based on:
- Previous research findings
- Existing psychological theories
- Observations of behaviour
- Gaps in current knowledge
- Personal experience and curiosity
Types of Hypotheses
In psychological research, we use different types of hypotheses depending on what we're trying to test. Understanding these differences is crucial for designing good research.
📝 Experimental Hypothesis
Also called the alternative or research hypothesis. It predicts that there will be a relationship between variables.
Example: "Listening to music while studying will improve memory recall."
❌ Null Hypothesis
Predicts that there will not be a relationship between variables. It's what researchers try to disprove.
Example: "Listening to music while studying will have no effect on memory recall."
↔ Directional vs Non-directional
Directional: Specifies the direction of the relationship (better/worse, increase/decrease).
Non-directional: Predicts a difference but doesn't specify the direction.
Writing Good Hypotheses
A well-written hypothesis is clear, specific and testable. It should identify the variables being studied and how they relate to each other.
Operationalising Your Variables
One of the most important skills in writing hypotheses is operationalisation - defining exactly how you'll measure your variables.
Example: From Vague to Specific
Vague hypothesis: "Stress affects memory."
Operationalised hypothesis: "Participants who complete a timed mental arithmetic task (stress condition) will recall fewer words from a 20-item word list compared to participants who sit quietly for the same period (control condition)."
Notice how the operationalised version specifies:
- How stress will be induced (timed mental arithmetic)
- How memory will be measured (recall of words from a list)
- What will be compared (stress condition vs control condition)
Common Mistakes When Writing Hypotheses
Even experienced researchers can make mistakes when formulating hypotheses. Here are some common pitfalls to avoid:
⚠ What to Avoid
- Being too vague: "Social media affects teenagers."
- Including unmeasurable concepts: "Playing violent video games makes people's souls darker."
- Making untestable claims: "Dreams are messages from another dimension."
- Confusing correlation with causation: "Ice cream consumption causes swimming pool drownings."
- Including too many variables: "Age, gender, education level and personality type will affect reaction time, accuracy and enjoyment of puzzle-solving."
✅ Good Practice
- Be specific: "Teenagers who use social media for more than 3 hours daily will report higher anxiety scores on the HADS scale compared to those who use it for less than 1 hour daily."
- Use measurable variables: "Participants who play the violent video game will show higher scores on the Buss-Perry Aggression Questionnaire than those who play the non-violent game."
- Make it testable: "People who record their dreams will report more vivid dream recall than those who don't."
- Be clear about relationships: "There will be a positive correlation between hours of studying and exam scores."
- Keep it focused: "Female participants will have faster reaction times than male participants when completing the Stroop test."
Evaluating Hypotheses
After conducting research, psychologists need to evaluate their hypotheses based on the evidence collected. This involves statistical analysis to determine if results are significant.
The Role of Statistical Testing
Statistical tests help researchers decide whether to accept or reject the null hypothesis. The process typically involves:
- Setting a significance level (usually p < 0.05)
- Conducting appropriate statistical tests
- Interpreting the results in relation to the hypotheses
Case Study Focus: Loftus and Palmer (1974)
In their famous eyewitness testimony experiment, Loftus and Palmer had a clear hypothesis about how language affects memory:
Hypothesis: "Participants who are asked 'How fast were the cars going when they smashed into each other?' will estimate higher speeds than participants asked 'How fast were the cars going when they hit each other?'"
This hypothesis was:
- Clear about the variables (verb used vs speed estimate)
- Directional (predicted higher speeds with "smashed")
- Easily testable through an experiment
- Operationalised (specified exactly what would be measured)
The results supported their hypothesis, showing how well-formulated hypotheses can lead to important psychological discoveries.
Practice Writing Hypotheses
The best way to get better at writing hypotheses is through practice. Here are some research questions to try turning into properly formulated hypotheses:
📖 Research Questions
- Does listening to classical music affect concentration?
- Are people more likely to help others when they're in a good mood?
- Does caffeine consumption affect reaction time?
- Do power poses influence confidence levels?
- Is short-term memory capacity different between age groups?
💭 Example Answer
Research question: Does listening to classical music affect concentration?
Experimental hypothesis: "Participants who complete a proofreading task while listening to classical music will identify more errors than participants who complete the same task in silence."
Null hypothesis: "There will be no difference in the number of errors identified between participants who complete a proofreading task while listening to classical music and those who complete it in silence."
Summary: Key Points to Remember
- A good hypothesis is specific, testable and clearly states the relationship between variables
- Always operationalise your variables by defining exactly how they'll be measured
- The null hypothesis predicts no relationship and is what researchers try to disprove
- Directional hypotheses specify the expected direction of the relationship
- Avoid vague terms, unmeasurable concepts and overly complex predictions
- Statistical testing helps determine whether to accept or reject hypotheses