This question has following choices to choose the answer from:
The larger sample would have a center closer to the true population parameter.
The larger sample would have less sampling variability.
The smaller sample would have less sampling variability.
The statistics of the smaller sample have more chance for bias than those of the larger sample.
I believe that the correct answer from the 4 choices would be the last one:
"The statistics of the smaller sample have more chance for bias than those of the larger sample."
This is because in a smaller sample, there is a high probability that not all parts or portion of the sample population is represented hence resulting in bias. This is especially true for distribution types of data wherein extreme values exist. Following that bias would already be effect on the center and the sampling variability. So bias always comes first.