Final answer:
A histogram's disadvantage in detecting outliers is its lack of granularity because data points are grouped into bins, which can hide extreme values. Methods like calculating the IQR or looking at standard deviations might be more precise for identifying outliers, although caution is needed for skewed data.
Step-by-step explanation:
The disadvantage of a histogram in outlier detection is d) Lack of granularity in data representation. Histograms group data into bins, which can obscure the presence of outliers. Due to the aggregation of data into intervals, extreme values may blend into the overall pattern of the histogram, making it difficult to pinpoint individual data points that are far away from others. The process of selecting interval sizes for these bins can further mask outliers, especially if the intervals are too wide.
For outlier detection, methods such as calculating the interquartile range (IQR) and identifying values that are more than 1.5 times the IQR above the third quartile or below the first quartile, or using standard deviation criteria, may provide more precision when looking for outliers. However, these methods should be used with caution when the data is skewed, as the outliers can influence measures like the mean and standard deviation and thus may give a misleading picture of the data's standard deviation.
Moreover, while histograms are useful for discerning the overall distribution, such as identifying whether data is skewed to the left or right, the granularity of a histogram is limited by its bins. Subsequently, when high granularity or individual data point analysis is required, other graphical representation methods such as scatter plots or box plots might be more effective.