Kuskal-Wallis

Kruskal-Wallis is used to determine if there is a significant difference between the medians of the different levels of your manipulated variable.​

Null Hypothesis:
"There is not a significant difference in the medians between the groups; any observed differences may be due to chance and sampling error."

For example:  

• There is no significant difference between the number of birds at the different locations; the differences we see in the means of the groups may be due to chance and sampling error.

Alternative Hypothesis:
"There is a significant difference in the medians between the groups; the observed differences are most likely not due to chance or sampling error."

For example:  

• There is a significant difference between the number of birds at the different locations; the difference we see in the means of the groups is mostly likely not due to chance or sampling error.

Kruskal-Wallis calculators such as this one will return a p-value.   A p-value of less than 0.05 means that the null hypothesis is rejected (that there is a significant difference among your groups). However, you do not know which group or groups are causing this to be true. If there is at least one significant difference among the groups, then the p-value will be <0.05.

​Therefore, if the p-value of the Kruskal-Wallace test is significant, it can be followed by a Dunn's post hoc test to examine differences between each possible pair of groups.  The Post-hoc tests to compares each group to each other group to see if there is a significant difference between then.  A p-value will be provided for each possible pairwise comparison between experimental groups.