Any hypothesis needs to be written in a way that it can:
- Be tested using evidence.
- Be falsified (found false/wrong).
- Provide measurable results.
- Provide yes or no answers.
For example, the following hypothesis can be tested and provides yes or no answers:
“The percentage of deformed frogs in five ponds that are heavily polluted with a specific
chemical X is higher than the percentage of deformed frogs in five ponds without chemical
X.”
Test Your Hypothesis
Thenextstepistocountthehealthyanddeformedfrogsandmeasuretheamountofchemical
X in all the ponds. This study will test the hypothesis. The hypothesis will be either true
or false.
An example of a hypothesis that is not testable would be: ”The frogs are deformed because
someone cast a magic spell on them.” You cannot make any predictions based on the defor-
mity being caused by magic, so there is no way to test a magic hypothesis or to measure
any results of magic. There is no way to prove that it is not magic, so that hypothesis is
untestable and therefore not interesting to a scientist.
Analyze Data and Draw a Conclusion
If a hypothesis and experiment are well designed, the experiment will produce measurable
results that you can collect and analyze. The analysis should tell you if the hypothesis is
true or false.
Example:
Your results show that pesticide levels in the two sets of ponds are statistically different, but
the number of deformed frogs is almost the same when you average all the ponds together.
Your results demonstrate that your hypothesis is either false or the situation is more com-
plicated than you thought. This gives you new information that will help you decide what
to do next. Even if the results supported your hypothesis, you would probably ask a new
question to try to better understand what is happening to the frogs and why. When you are
satisfied that you have accurate information, you share your results with others.
You will probably revise your hypothesis and design additional experiments along the way.