Again, noisy data can still be useful. For instance, generate a perfect single-variable normal distribution, sample along the x-axis and perturb each point randomly in the y direction either up or down. Depending on the range of random values used to move the sampled points, you can still see the underlying distribution even though the data is noisy.

Two possible arguments you might make:

1) The data is noisy and the paper's authors haven't collected enough data to account for the amount of noise. Usually people will do something like null-hypothesis significance testing to measure this.

2) The noise isn't uniformly random and has some underlying bias that is affecting the results.