pvalues between coefficients
Our main goal involves presenting evidence of whether there is a difference between the calculated coefficient values. For example, if two 95% Wallace's confidence intervals overlap, can they still be considered statistically different?
To reject the hypothesis that there is no difference between coefficients, we need to perform a significance test. The Comparing Partitions user has now access to a pvalue, helping to decide if the null hypothesis should or should not be rejected. The pvalue is the probability of wrongly rejecting the null hypothesis if it is in fact true.
H_{0}: There is no difference between the coefficients;
H_{1}: There is a difference between the coefficients.
One rejects the null hypothesis if the pvalue is smaller than or equal to the significance level. By convention, if pvalue≤0.05 one can reject the null hypothesis. This means that except for a (perhaps important) 5% chance of being wrong the researcher can reasonably reject the hypothesis that the two results are the same.
The pvalue herein presented is calculated according to the jackknife pseudovalues resampling method.
Example 1  Wallace coefficient

We want to test if the highlighted values of Wallace coefficients are statistically different (microbial typing test data):
After the resampling procedure choose in the dropboxes:
After clicking "pvalue" a new table is displayed:
The resulting pvalue is higher that 0.05. Therefore the null hypothesis
H_{0}: W_{PFGE Sma80 → T type} = W_{PFGE Sma80 → T type + emm type}
cannot be rejected.
Example 2  Simpson's Index of Diversity

We want to test if the Simpson's Index of Diversity values for PFGE SFi68 and T type (highlighted) are statistically different (microbial typing test data).
The pvalue for the comparison of the highlighted methods is <0.01. Because this value is lower than 0.05, the null hypothesis
H_{0}: Simpson's ID_{PFGE SFi68} = Simpson's ID_{T type}
can be rejected, meaning that the values are unlikely to be equal.
Previous version
In a previous version of this website, the user was able to calculate confidence intervals and pvalues according to the bootstrap resampling method. We recently evaluated different jackknife and bootstrap methods for calculation of CIs for pairwise agreement measures. our results shown that the jackknife pseudovalues is the most suited resampling method (Severiano et al, 2011) and therefore should be used. Here you can find a previous version of this tool where you can calculate the 95% CI and the pvalues by bootstrap.