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We have presented a “data scenario.� In each case, decide which type of grouping (single-value, limit, or cutpoint) is probably the best.
Giant Tarantulas. The carapace lengths, to the nearest hundredth of a millimeter, of a sample of 50 giant tarantulas
The Correct Answer and Explanation is:
Correct Answer: Single-value grouping
Explanation
In the case of the Giant Tarantulas’ carapace lengths, which are measured to the nearest hundredth of a millimeter, the most appropriate type of grouping is single-value grouping.
Why single-value grouping is appropriate:
Single-value grouping is ideal when the data values are discrete and recorded to a precise decimal place, especially when the range of values is not excessively wide and the values are repeated across observations. Since the carapace lengths are measured to 0.01 mm precision and the sample consists of only 50 tarantulas, the data will likely contain repeated measurements, or at least a small number of distinct values. This makes it feasible and informative to group each distinct measurement as its own category or row in a frequency distribution.
For example, if several tarantulas have carapace lengths of 25.43 mm, 25.47 mm, 25.52 mm, and so on, we can count how many times each exact measurement occurs. This is useful for identifying the mode, understanding data clustering, and spotting exact measurement frequencies, which can be valuable in biological research for identifying growth patterns, species variation, or measurement consistency.
Why not limit or cutpoint grouping?
- Limit grouping is more suitable when data are recorded as whole numbers, especially if the range is wide. For example, if tarantula lengths were measured only in whole millimeters, it might make sense to create ranges like 20–24 mm, 25–29 mm, etc.
- Cutpoint grouping is generally used for continuous data, especially when decimal places matter, and the number of values is large, like in thousands. It involves defining intervals like “25.0 to less than 25.5.” However, for a small sample size of 50, this might result in too many intervals with few or no data points, reducing interpretability.
Conclusion:
Given the precision (hundredth of a millimeter) and modest sample size (50), single-value grouping provides the most clarity and statistical utility for this dataset.
