Hypotheses and Predictions
The word “hypothesis” can mean different things depending on the context. In IB Biology, there are two very distinct categories of hypotheses:
1. The research/experimental hypothesis - an idea that can be tested through experiments or observation. The research hypothesis can be written in either a generalizing or explanatory format.
Prediction statements can be formed from hypotheses. A prediction says what will happen in an experiment if the hypothesis is correct. For example, an experiment could be designed to test the generalizing hypothesis that students who study regularly tend to perform better on exams. Randomly assign a group of students to study regularly and another group to not study regularly (the control group). Give the students an exam and compare average scores between the two groups of students. The prediction would be that students who study regularly will perform better on the exam than those who do not.
2. The statistical hypotheses - the null and alternative hypothesis of inferential statistical tests. The statistical hypotheses are statements about whether a pattern/trend/difference is SIGNIFICANT (meaning, likely due to more than chance in sampling). The statistical hypotheses do not necessarily provide support for or against the research hypothesis that was tested; they just indicate whether chance alone likely could be the reason for the results. The statistical hypotheses are:
1. The research/experimental hypothesis - an idea that can be tested through experiments or observation. The research hypothesis can be written in either a generalizing or explanatory format.
- Generalizing hypothesis: a description of a pattern. For example: Students who study regularly tend to perform better on exams than those who do not.
- Explanatory hypothesis: a possible explanation for why a pattern exists. For example: Students who study regularly perform better on exams because consistent review reinforces memory retention.
Prediction statements can be formed from hypotheses. A prediction says what will happen in an experiment if the hypothesis is correct. For example, an experiment could be designed to test the generalizing hypothesis that students who study regularly tend to perform better on exams. Randomly assign a group of students to study regularly and another group to not study regularly (the control group). Give the students an exam and compare average scores between the two groups of students. The prediction would be that students who study regularly will perform better on the exam than those who do not.
2. The statistical hypotheses - the null and alternative hypothesis of inferential statistical tests. The statistical hypotheses are statements about whether a pattern/trend/difference is SIGNIFICANT (meaning, likely due to more than chance in sampling). The statistical hypotheses do not necessarily provide support for or against the research hypothesis that was tested; they just indicate whether chance alone likely could be the reason for the results. The statistical hypotheses are:
- Null hypothesis (H 0 ). The null hypothesis states that the results could be due to chance, that there is no significant relationship/difference compared to what could have resulted from random chance in sampling.
- Alternative Hypothesis (H 1 ). The alternative hypothesis states that results are not likely due to chance, that there is a significant relationship/difference compared to what could have resulted from random chance in sampling.
