P(x=mean) of normal PDF with low sigma - not allowed?

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Discussion Overview

The discussion centers around the properties of the normal probability distribution, particularly the implications of evaluating the probability density function (PDF) at the mean when the standard deviation (σ) is low. Participants explore the mathematical behavior of the PDF and the intuition behind changes in scale affecting probabilities.

Discussion Character

  • Technical explanation
  • Conceptual clarification
  • Debate/contested

Main Points Raised

  • One participant questions the validity of P(X=μ) when σ is less than (1/sqrt(2π)), suggesting that it leads to a probability greater than 1.
  • Another participant clarifies that for continuous probability density functions, the probability of a specific value is 0, indicating a misunderstanding in the original query.
  • A third participant reinforces that the probability density function should be interpreted as a density over an interval rather than a point probability.
  • A later reply acknowledges the previous contributions and expresses gratitude for clarifying the misunderstanding regarding the nature of probabilities in continuous distributions.
  • One participant notes that the cumulative probability P(X ≤ μ) can be calculated as an integral of the PDF, which yields a result of 1/2, independent of mean or standard deviation.

Areas of Agreement / Disagreement

Participants generally agree on the interpretation of probabilities in continuous distributions, particularly that P(X=x) is 0 for any specific value. However, there is an ongoing exploration of the implications of low σ and the effects of scaling on the PDF, indicating some unresolved nuances.

Contextual Notes

The discussion highlights the dependence on the definitions of probability density and the interpretation of continuous distributions, as well as the implications of scaling on standard deviation and mean.

Who May Find This Useful

Readers interested in probability theory, particularly those studying normal distributions and the properties of continuous probability density functions.

nomadreid
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P(x=mean) of normal PDF with low sigma -- not allowed?

About the normal probability distribution: with formula
P(X=x) = (1/(σ*sqrt(2π))*exp(-(x-μ)2/2σ2), what happens when you look at P(X=μ) if σ<(1/sqrt(2π))? You get P(X=μ)>1, an absurdity. What is going on?

Second question is one about intuition: suppose μ=0, then why would a scale change of the horizontal axis (say by changing units from meters to kilometers) , which would also change σ, affect the probability of the mean, which it would by the formula?
 
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the probability is 0 since what you looked at is a probability density p(X in x,x+dx) equals f(x)dx, where f is your gaussian function
 


For any continuous PDF, the probability that x is equal to any specific value, rather than in a given interval or set, is 0.
 


Thanks, jk22 and HallsofIvy. My reaction upon reading your replies and thinking for a couple of seconds was, "Of course. Stupid of me." So thanks for that destruction of the mental block.
 


What you can calculate is
P(X \le \mu) = \int_{-\infty}^{\mu} p(x) \, dx
where p(x) the PDF. Of course, this will give you 1/2 independent of the mean or standard deviation.
 

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