Hi all,

Recently I did a field study where I investigated recycling behaviour in the office. We did a baseline and post-intervention measurement of the respons rate (i.e. how much of the total amount of a certain type of waste ends up in the right bin?). For 2 x 2 weeks we collected and analyzed all the trash on a daily base, resulting in 10 + 10 (baseline + post) datapoints.

On four floors we tested interventions to improve reclycling and we compared these to two control conditions. On a small N=20 we found strong effects (Cohen's d > .80). For some effects the p-value was marginally significant (.05-.10) but we still reported those effects. I was taught that if you find strong effects on a small N that are close to being significant, you can assume the effect is actually there. After all, the p-value will automatically decrease when N increases.

We also reported effects with a p-value between .10-.15 with an explicit diclaimer that these effects should be interpreted with great caution and that a follow-up study is required.

Someone, however, heavilly criticized us for reporting effects that have a p-value above .05. I personally think that this person is too rigid concerning the p-value considering the small N and the large effects. But maybe I'm wrong and I shouldn't have reported these results.

Can anybody tell me whether it was justified to report these results or not (also taking into account the disclaimer we used)? Thanks!

Best,
Danny