Using an independent samples t test, I am comparing the performance of 2 groups on an activity, based on the percentage of items correct (the percentage of total items correct, as well as percentage of items correct for four sub-domains of the activity). The n of one group is 23, and the n of the other is 34.
Due to a ceiling effect, my data are non-normally distributed for both groups. At the recommendation of a statistician/professor, to correct for this problem I bootstrapped the t tests in SPSS using 95% bias corrected confidence intervals based on 1,000 replications. The smaller group has a larger variance, so I am interpreting the analyses using Welch's correction ("equal variances not assumed").
The SPSS output for the bootstrapped t tests produces a table with some p values left blank under the Sig. (2-tailed) column...Why is this? And what do I report instead of these missing bootstrapped p values? I just want to be consistent in how I explain which results were significant.
All of the Lower and Upper BCa 95% confidence intervals ARE listed in the table, but I'm not sure how to succinctly communicate significance using only the confidence intervals (Though I know that if the interval does not include 0 then it can be concluded that the difference between the 2 groups is significant).