Average a dataset's smallest and largest values to locate the center of its range. The calculation trail makes the reported midrange easier to reproduce.
Midrange is useful for a quick center estimate when endpoints are meaningful, such as a tolerance interval or a bounded measurement span. It is also a convenient check on the center of a symmetric range.
Midrange is the arithmetic mean of a dataset's minimum and maximum. It uses only the two endpoints, so it describes the center of the observed range rather than the balance point of every observation.
For a Midrange audit, retain Data values and the fixed condition. Decide the likely direction of Midrange before rerunning Midrange. Change only the fixed condition; the response in Midrange can then be traced within the Midrange setup.
Because only two observations determine midrange, one extreme outlier can move it sharply while every interior value remains unchanged. It should not replace mean or median when the full distribution matters. If the Midrange assumptions do not fit, consider mean and median.
Scan the list for its smallest and largest values, add those endpoints, and divide by two. Sorting is optional, but it makes endpoint selection easier to verify in a short dataset.
For 12, 18, 21, 27, and 42, the minimum is 12 and maximum is 42. Their average is (12 + 42)/2 = 27, even though the arithmetic mean of all five values is 24. This Midrange example can be compared with spread around the mean.
Substitute the Midrange solution into Data values. This Midrange check rejects false Midrange branches and forbidden denominators.
Choose an easy Data values value before running Midrange. Predict the result, then compare it with the Midrange output.
No. Range subtracts minimum from maximum; midrange averages them.
No. Only a change to the minimum or maximum can change it.
It always lies halfway between the observed endpoints, though it need not be an observed value.
It can be poor when endpoints are outliers or the distribution is strongly skewed.