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Writing hypotheses

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A hypothesis is a precise, testable prediction. You need to be able to write both an alternative hypothesis and a null hypothesis.

Alternative hypothesis

The alternative hypothesis predicts that there will be a difference (or relationship), caused by the IV.
It must include the IV and the DV, both operationalised.
Example: 'Participants who learn a list of 20 words in silence will recall more words than participants who learn the list while music is playing.'
It can predict a direction (more/less) or simply that there will be a difference.

Null hypothesis

The null hypothesis predicts that there will be no difference (or relationship), and that any difference found is due to chance.
Example: 'There will be no difference in the number of words recalled out of 20 between participants who learn in silence and those who learn with music; any difference will be due to chance.'
Research tests the null hypothesis: if the results show a clear difference, the null hypothesis is rejected and the alternative is accepted.

Correlations

For a correlation, hypotheses predict a relationship between two co-variables, not a difference.
Alternative: 'There will be a positive correlation between hours of sleep and score on a memory test.'
Null: 'There will be no correlation between hours of sleep and score on a memory test.'
Checklist for any hypothesis: is it testable? Does it name both variables clearly? Does it say what is predicted?
Worked example

Write a null hypothesis for a study testing whether caffeine affects reaction time.

  1. The null hypothesis predicts no difference.
  2. It should name the IV (caffeine or no caffeine) and the operationalised DV (reaction time in milliseconds).

Answer: There will be no difference in reaction time (in milliseconds) between participants who drink caffeine and those who do not; any difference will be due to chance.

Key idea

An alternative hypothesis predicts a difference (or relationship) caused by the IV. A null hypothesis predicts no difference (or no relationship), with any difference due to chance. Both must include clearly operationalised variables.

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