High School Is there a relationship between the harmonic mean and the standard deviation?

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SUMMARY

The discussion centers on the relationship between the harmonic mean (HM) and standard deviation (σ) using a sequence of numbers. The arithmetic mean (AM) remains constant at 100 while the HM decreases as the middle values become more extreme, illustrating that HM is sensitive to lower values. The conversation explores the mathematical relationship, proposing that HM can be expressed as a function of σ, particularly in a normal distribution context. The participants seek a rigorous mathematical proof to establish this relationship.

PREREQUISITES
  • Understanding of harmonic mean and arithmetic mean
  • Familiarity with standard deviation and its significance
  • Knowledge of normal distribution (N(μ,σ²))
  • Basic concepts of mathematical expectation (E)
NEXT STEPS
  • Research the mathematical properties of harmonic mean and its applications
  • Study the derivation of relationships between HM and standard deviation
  • Explore the implications of fluctuations in data on HM and AM
  • Investigate statistical proofs involving expectations and distributions
USEFUL FOR

Mathematicians, statisticians, data analysts, and anyone interested in the relationships between different statistical measures and their implications in data analysis.

Feynstein100
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I was doing a thought experiment on the harmonic mean. Let's say we have a sequence of 4 numbers:

100, 110, 90, 100

The arithmetic mean (AM) of this sequence is 100 but the harmonic mean (HM) is 99.4975.
Let's imagine that the initial and final values remain constant but the middle ones get more and more extreme.

100, 120, 80, 100

The AM is still 100 but the HM is now 97.9592

100, 130, 70, 100

AM = 100, HM = 95.2880

and so on

I think I understand why this happens. The AM is symmetric, if you will. So an equal movement above the mean will cancel out an equal movement below it. However, the HM is more skewed toward the lowest value in the sequence. As such, it only takes one low value to bring the HM down. And with each step, we have a lower minimum and thus a lower HM. This is basically a crude verbal proof. Is there a more rigorous, mathematical one?

I'm also interested to know if there's a relationship between the HM and the fluctuation (I guess the arithmetic standard deviation is a good way to represent this). I mean, there has to be, as higher fluctuations mean higher likelihood of lower values and thus a lower HM, right?

Basically, HM = f(σ)?
 
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Imagine a variable x with a distribution N(μ,σ2), i.e. a Normal distribution with mean μ and variance σ2. Think of x as μ + δ where δ = N(0,σ2), and (for simplicity) σ << μ. Can you derive an (approximate) expression for HM, where HM = 1/E(1/x)?
 
mjc123 said:
Imagine a variable x with a distribution N(μ,σ2), i.e. a Normal distribution with mean μ and variance σ2. Think of x as μ + δ where δ = N(0,σ2), and (for simplicity) σ << μ. Can you derive an (approximate) expression for HM, where HM = 1/E(1/x)?
Sounds interesting. But what exactly is E in this case? 😅
 
The expectation (mean of the distribution).
 
mjc123 said:
The expectation (mean of the distribution).
Ah okay but isn't that literally the definition of the harmonic mean? Not sure what we'd get by proving it. I mean, I'm trying to find a relationship between HM and σ, not HM and E.

Also, in the example, the E remained constant whereas the HM kept decreasing, exactly illustrating my point. Although, oh wait. That was the AM, not E(1/x). My bad 😅
 
I wasn't asking you to prove HM = 1/E(1/x), but to use that definition to derive an expression for HM in terms of μ and σ.
 

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