Ignore empty end classes. Subtract the lower boundary of the first occupied class from the upper boundary of the last occupied class. The actual minimum and maximum lie somewhere within those classes, so the true range is not known and may be smaller.

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Treat every value in a class as its midpoint, multiply by the class frequency, and divide the product total by the observation total. Exact values within each class are unknown, so this is an estimate.

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Use frequency-weighted midpoints for the mean. For the range estimate use the lowest occupied lower bound and highest occupied upper bound. Neither result recovers the original exact measurements.

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The midpoint product total estimates the sum of all recorded times. Divide that sum by the observation count to estimate the mean. The sum and mean answer different questions.

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Class frequencies are unequal, so treating each class as one observation gives the wrong weights. Multiply each midpoint by its frequency and divide by the total number of observations.

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Calculate a separate weighted midpoint total for each group and divide each by its own observation count. The estimated means are equal. These are grouped-data estimates, not exact raw-data means.

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The estimated means are equal.

Write the weighted-midpoint mean equation with the unknown frequency in both numerator and denominator. Simplify and solve; check that the result is a non-negative whole number.

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Calculate a separate weighted midpoint total for each group and divide each by its own observation count. B has the larger estimated mean. These are grouped-data estimates, not exact raw-data means.

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B has the larger estimated mean.

Write the weighted-midpoint mean equation with the unknown frequency in both numerator and denominator. Simplify and solve; check that the result is a non-negative whole number.

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For the midpoint estimate, weight 5, 15 and 25 by the counts. The smallest possible sum uses each lower class boundary. The upper-bound sum uses the excluded upper boundaries, so it is not attained. Compatible values at the lower boundaries give mean thirteen; values one below each upper boundary give mean twenty-two. Thus eighteen can lie above or below the actual mean.

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The midpoint estimate can be too high or too low.