I ran a simple randomized experiment with 1 control and 1 experimental condition (final N = 80). The dependent variable (frequency of a shown behavior) is clearly not normally distributed so I thought about bootstrapping my analysis (an independent t-test).
The t-test without bootstrapping resulted in a significant effect between the conditions (p < .05). Using SPSS, the p-value based on bootstrapping (5000 resamples) is only marginally significant (p < .10). However, the 95% confidence interval does not include zero.
I just apply statistical methods and sometimes I don't know what is actually right (unfortunately!). But that's why I ask this question. When I tried to learn how Bootstrapping works, I thought that one has to look at the confidence intervals to detect whether an effect is not zero. In my example above, the 95% CI does not accompany with the bootstrapped p-value. So I don't know whether I should report the bootstrapped CI and/or the bootstrapped p-value and/or the typical unbootstrapped p-value.
What would you say?