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1. Having more samples (which you said is pretty much constrained);
2. Settling for a lower test power (i.e. have a lower chance to see a significant result if there is indeed a saving);
3. Choosing a lower significance level (i.e. reject H_0 when p<0.1 instead of 0.05, risking more false positives); or
4. Praying and hoping the test subjects' water usage behaviour (and hence the water savings) are more consistent, reducing the spread of the responses.

1. Having more samples (which you said is pretty much constrained);
2. Settling for a lower test power (i.e. have a lower chance to see a significant result if there is indeed a saving);
3. Choosing a higher significance level (i.e. reject H_0 when p<0.1 instead of 0.05, risking more false positives); or
4. Praying and hoping the test subjects' water usage behaviour (and hence the water savings) are more consistent, reducing the spread of the responses.

which suggest to me that it is easier for you to estimate the variance / standard deviation than the effect size. Furthermore, I (as a layman in water technologies) would imagine is it easier to control how much water a device can save on average than how spread out the water savings are.

which suggest to me that it is easier for you to estimate the variance / standard deviation than the effect size. Furthermore, I (as a layman in water technologies) would imagine is it easier to influence how much water a device can save on average than how spread out the water savings are.

Note: The question was subjected to multiple round of edits, when other answers were made in between. This answer is made after Edit 2 was posted.

Note: The question was subjected to multiple round of edits, when other answers were made in between. This answer is made after Edit 2 was posted, and refrained from dealing with the part on Wilcoxon and ANOVA as it is unlikely to add on what existing answers have.