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The appropriate number of histogram classes is commonly determined using Sturges' formula, which is suitable for data sets that follow a normal distribution. Sturges' formula suggests that the number of classes can be calculated as \( k = 1 + 3.322 \log_{10}(N) \), where \( N \) is the number of observations.

In this case, for 100 observations, applying Sturges' formula gives us:

\[

k = 1 + 3.322 \log_{10}(100) = 1 + 3.322 \times 2 = 1 + 6.644 \approx 7.644

\]

Rounding this result indicates that you might want around 8 classes. Since the guidelines for histogram class number often suggest a range to accommodate variability in data distribution, option B, which suggests 6-10 classes, fits well within this range. Therefore, it is an appropriate choice for creating a histogram that effectively reflects the structure of the data without either over-simplifying or creating excessive detail.

Further, having too few classes can mask the distribution of the data, while too many can lead to an excessive amount that might misrepresent it. Hence, a