Power analyses for repeated measures designs are usually done with Monte Carlo simulations: you generate fake but plausible data sets with a hypothesized effect size, then analyze these data sets and count how often the effect is detected.

I agree that N=34 seems low. The effect would have to be quite large to be detected and there may be a risk of Type M (magnitude overestimated) and Type S (incorrect sign) errors. The results should therefore be interpreted in the light of a power analysis.

Statistical analysis exploratory studies tho get some ballpark numbers. They didn't even estimate power in this case. If you have to rely on statistics to observe effect, the effect is small.

The most likely biases and noise comes from experimental design and other factors related to the study.

In exploratory science doing two different studies with N=30 is much better than doing one study with N=60.