Demonstrating the type of relationship shown above does not differentiate between these two hypotheses. It is only possible to demonstrate causality if experiments are carefully designed, using techniques such as the random assignement of cases to treatments and control over the experimental conditions. However, failure to reject Ho is not evidence for the absence of a biological effect. Null hypotheses are never accepted. The magnitude of an effect that can be demonstrated by a statistical test is related to the power of the test.
Difficulties arise when the 'signal' is weak relative to the amount of 'noise'.
Sample sizes required to demonstrate small, but biologically important, effects may be impractical because of logistics or biology. If you have reason to believe that the effect is small, but important, an unthinking reliance on significance tests may not be desirable. This is also known as external validity. Suppose that you have discovered a relationship between spring weather and breeding performance of the merlin Falco columbarius in the South Pennines. One approach to demonstrating the generality of your conclusions is to test your model with some novel data.