Final answer:
The statement is (b) false. When counts cannot go below 0, histograms are often right skewed, not left skewed.
Step-by-step explanation:
The statement 'Histograms are often skewed to the left when the count cannot go below 0' is false. When count data cannot go below 0, you often see histograms that are skewed to the right because there's a natural lower bound at 0. A right skewed, or positively skewed, histogram will have a tail that extends to the right. In such cases, there are more lower values, and the mean is typically greater than the median, which is greater than the mode. This is opposed to left skewed distributions where the mean is typically less than the median.
As for the handling of boundaries in histograms, it's important to note that class intervals typically include the left boundary value but not the right, except for the first interval where both boundaries are included. This ensures that each data value is counted in one and only one class interval. Different approaches to setting histogram boundaries may result in different histogram shapes, but the rule for counting boundary values remains relatively standard.