Final answer:
Without the specific image of the boxplot, one can deduce the type of distribution by assessing the plot's symmetry and comparing the positions of mean, median, and mode.
Step-by-step explanation:
To ascertain the distribution of data from a boxplot, we need to examine the plot's symmetry and the relative positions of its mean, median, and mode. A positively skewed distribution will have a longer right tail, suggesting that the mean is greater than the median. On the contrary, a negatively skewed distribution shows a longer left tail, indicating that the mean is less than the median. In the case of a normally distributed data set, the mean, median, and mode would be approximately equal, and the boxplot would be symmetric. Lastly, a bimodal distribution refers to one that has two modes, or peaks, which can sometimes be observed in a boxplot if there are two distinct clusters of data.
Without the specific boxplot image, it is impossible to provide a definitive answer to the question. However, assessing skewness and modality in a boxplot generally involves looking for signs of symmetry or lack thereof, and understanding whether the data clusters around one or multiple values. For a distribution such as the performance in a running distance (an example mentioned above), which many can do up to a short distance but few can continue as the distance increases, would typically resemble an exponential or positively skewed distribution.