Generally, when the outcome of some 'experiment' depends on many independent (and ideally identically distributed) random factors, the distribution of outcomes tends to be Gaussian. This result is known as the Central Limit Theorem. It is likely to be the case here.
My own observation is that bubble size under the same conditions (eg same depth in the liquid) tends to be quite uniform with very little variation.

On page 2 of The Quasi-Static Growth of CO2 Bubbles it is stated that bubble radius $R(t)$ during nucleation is observed to grow in proportion to $\sqrt{t}$ where $t$ is time. When bubbles reach a certain radius $R_0$ they have enough buoyancy to detach from the container and rise to the surface, continuing to expand as they do so. If we look only at attached bubbles, then $dt\propto RdR$. That is, the amount of time $dt$ which a bubble spends with radius $R$ to $R+dR$ is proportional to $R$. The probability of finding a bubble within $dR$ of radius $R$ is proportional to $dt$, therefore the distribution is expected to be proportional to $R$ from $R=0$ to $R_0$.

In the above chart I have measured the diameters (in pix) of all 59 bubbles in sufficient focus in the previous image, and averaged and plotted frequency against radius. The distribution is very approximately triangular, as predicted.
Raw data : diameters of bubbles in pix
18 18 22 17 16 16 15 6 7 23 17 18 20 23 21 17 19 19 21 9 22 24 22 9 17 21 20 6 20 19 5 28 25 22 18 23 18 22 18 12 9 11 12 24 23 26 23 20 22 14 12 13 22 14 19 22 8 6 11
If fitted with Weibull, then we have the following fit (using the data above).
