Hypothesis Testing

Hypothesis testing is an essential component of statistics and a proper understanding is critical to understanding our statistical analyses. Typically, we utilize a null and alternative hypothesis. 

Conceptually, a null hypothesis states that “there is nothing going on.” For example, there is no relationship between tree height and photosynthesis, or water quality does not affect larval amphibian development, or there are no significant differences in species composition between forest and grassland habitats.

On the other hand, alternative hypotheses state that “there is evidence of something going on.” We can create alternative hypotheses from the null hypotheses presented earlier: Tree height does have an effect on photosynthesis, water quality does affects larval amphibian development, or there are significant differences in species composition between forest and grassland habitats.

Hypothesis testing aims to provide evidence for the alternative hypothesis and reject the null hypothesis. However, we cannot prove either hypothesis to be completely true or false. Instead, we have enough data, and thus evidence, to support or not support the alternative.