Final answer:
The main issue with having too small bins in a histogram is that it creates a distorted data distribution, obscuring the overall shape and trends within the data set. Properly sized bins are crucial for accurately conveying the underlying patterns and determining the appropriate measure of center for the data.
Step-by-step explanation:
The problem with having too small bins in a histogram is c) Distorted data distribution. When bins are too small, the histogram may overrepresent the noise in the data, making it difficult to discern the actual distribution and patterns. This level of granularity can give a misleading impression of variability or trends within the data set. It does not necessarily lead to loss of data detail or overemphasis on outliers, and it does not prevent the detection of the mode. Instead, too small bins can obscure the overall shape of the data distribution due to excessive fragmentation.
Examining the shape of the data, having a reasonable number of not too small bins gives a more appropriate result, as it balances data representation with the underlying trends and patterns. A histogram with properly sized bins will help to identify the most appropriate measure of center for the data, which could be the mean, median, or mode, depending on distribution symmetry and presence of outliers.