Hypothesis Testing

Statistical Hypothesis Testing

A Hypothesis is a statement explaining that a causal relationship exists between an underlying factor (variable) and an observable phenomenon.    Often, after making an observation, you might propose some sort of tentative explanation for the phenomenon; this could be called your working hypothesis.    Because absolute proof is not possible, statistical hypothesis testing focuses on trying to reject a null hypothesis. 

A Null Hypothesis is a statement explaining that the underlying factor or variable is independent of the observed phenomenon—there is no causal relationship.    For example, in the leaf study-  an appropriate null hypothesis might be that the distributions of the leaf widths in sunny and shady habitats are the same—in other words, that there is no difference between the two populations. 

The Alternative Hypothesis might be that there is a size difference between the two populations.   Usually (but not always), an investigator is trying to find an alternative to the null hypothesis—evidence that supports the alternative hypothesis by rejecting the null (based on statistical tests).

In AP Biology:    After we perform statistical analysis,   we either Reject the Null Hypothesis (there is a statistical difference in our data) or we Fail to Reject the Null Hypothesis (there is no statistical difference in our data).  It is important to write it this way.  You do not say: " I accept the null hypothesis)