Hypothesis examples
01 // Same format, different subjects
A hypothesis follows the same if-then-because structure no matter what subject it's written for — only the variables and reasoning change. Seeing several worked examples side by side makes the pattern easier to copy for a new experiment than reading the rule alone.
02 // Examples by subject
| Subject | Hypothesis |
|---|---|
| Biology | If a bean plant gets 12 hours of light a day instead of 6, then it will grow taller after two weeks, because more light gives it more energy for photosynthesis. |
| Chemistry | If more baking soda is added to the same amount of vinegar, then the balloon will inflate to a larger size, because more baking soda produces more carbon dioxide gas. |
| Physics | If a pendulum's string is made longer, then its period will increase, because a longer string takes more time to complete each swing. |
| Environmental science | If a stream sample is collected closer to a farm's runoff, then its nitrate level will be higher, because fertilizer runoff carries nitrates into nearby water. |
| Human biology | If a person's resting heart rate is measured after climbing stairs instead of sitting, then it will be higher, because physical activity increases the heart's workload. |
03 // What makes each one testable
- Every example names one specific thing being changed (the independent variable) and one specific thing being measured (the dependent variable) — never both at once, and never something vague like 'health' or 'growth' with no measurement attached.
- The 'because' clause isn't just filler — it should be a real scientific reason, based on something already known, not a restatement of the prediction itself.
- A hypothesis doesn't have to turn out true to be well written. All five examples above are testable and falsifiable whether or not the data ends up supporting them.
04 // Questions people ask
Do all hypotheses have to use the exact words 'if, then, because'?
No — that's a template for learning the structure, not a required wording. What matters is that a hypothesis clearly states what's being changed, what's predicted to happen, and the reasoning behind that prediction, however it's phrased.
Can a hypothesis be wrong and still be a good hypothesis?
Yes. A hypothesis is judged by whether it's specific and testable, not by whether it turns out to be correct. A well-written hypothesis that the data doesn't support is still a successful, useful hypothesis.
What's a common mistake in writing a hypothesis?
The most common mistake is being too vague — writing something like 'fertilizer affects plant growth' instead of naming the specific independent variable, the specific measurable outcome, and the direction of the predicted change.