By definition, the harmonic mean of a dataset (x1, x2, ..., xn) is the reciprocal of the arithmetic mean of the dataset's reciprocals.
That is, MeanHarmonic=n/[1/x1+1/x2+...+1/xn]
The harmonic mean applies more accurately to certain situations involving rates. For example, imagine a blood donor fills a 250mL blood bag at 70mL/min on the first visit, and 90mL/min the second visit. What is the average rate at which the donor fills a bag? Let's break it down:
250mL @ 70mL/min = 3.571 mins total
250mL @ 90mL/min = 2.778 mins total
So 500mL total in (3.571+2.778) mins total = 500/6.349 = 78.753 mL/min
So the harmonic mean of 2/[1/70+1/90] = 78.750 gives a more accurate description of average rate, in this example, than the arithmetic mean (80mL/min).
This, however, only applies to rate. If, for example, you wanted to calculate average time to donate it would just be the arithmetic mean (3.571+2.778)/2 = 3.175 mins.
Chat with our AI personalities
The advantage of harmonic mean is that it is used to solve situations in which there are extreme data values to true picture. The disadvantage of it is that it can be time consuming to evaluate the data.
A body undergoes simple harmonic motion if the acceleration of the particle is proportional to the displacement of the particle from the mean position and the acceleration is always directed towards that mean. Provided the amplitude is small, a swing is an example of simple harmonic motion.
Springs, sound and musical instruments, electronic oscillators, alternating electric currents, that sort of thing.
The arithmetic mean, geometric mean and the harmonic mean are three example of averages.
The arithmetic mean is 140. The geometric mean is approx 138.56 and the harmonic mean is approx 137.14