Aquascope Facts

Null hypothesis

A null hypothesis is the opposite of a hypothesis and should express all the explanations that are not included in the hypothesis. In our example, the null hypothesis states that if we remove individuals of one species of mussel from the experimental areas, the remaining number of mussels will be unchanged or even decrease in number in comparison with the control areas. This can be statistically tested when we have counted the number of mussels within the different control and experimental areas. If we find evidence for an increase in the average number of mussels within the experimental areas (where the competitors were removed), then we kan reject the null hypothesis. We have falsified the null hypothesis and in this way we have excluded all possible explanations except for those that are expressed in the hypothesis. When we have obtained evidence from the experiment that the null hypothesis is incorrect, we can maintain that the hypothesis is correct. We can then accept the model: What was predicted in the model did happen. See figure 1.
    When we removed the competing mussels, the other species increased in number. It appears that the two species of mussel compete with one another - survival rate is less within one species when there are many of the other.

Keep the null hypothesis

Our experiment can in only one way result in a different outcome. That is when the evidence from the experiment does not enable us to reject the null hypothesis. If the average number of one species of mussel is equal to or less than the other in those areas where competitors have been removed when compared to the control areas, then we have no evidence that denounces the null hypothesis. In such a case, we must reject the hypothesis because we have no evidence that the number of mussels increased as predicted. Therefore, the model is incorrect. We have now falsified the hypothesis and therefore must find a new model that can explain why the pattern of mussel distribution is as seen in the beginning.
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Null hypothesis
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Bo Johannesson | Martin Larsvik | Lars-Ove Loo | Helena Samuelsson