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Home/ Questions/Q 6700899
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Editorial Team
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Editorial Team
Asked: May 26, 20262026-05-26T06:52:09+00:00 2026-05-26T06:52:09+00:00

I don’t know anything about statistics and it was difficult for me to find

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I don’t know anything about statistics and it was difficult for me to find A way to describe my question that was clear.

I am doing some initial research on a system that will measure the uniformity of electricity across a conductor. Basically we need to measure how evenly a signal is spread out on a surface.

I was doing research on how to determine uniformity of a data set and came across this question which is promising. However I realized that I don’t know what unit to use to express uniformity. For example, if I take 100 equally spaced measurements in a grid pattern on the surface of an object and want to describe how uniform the values are, how would you say it?

  • “98% uniform?” – what does that mean? 98% of what?
  • “The signal is very evenly dispersed” – OK, great… but there must be a more specific or scientific way to communicate that… how “evenly”? What is a numeric representation of that statement?

Statistics and math are not my thing so if this seems like a dumb question, be gentle…

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  1. Editorial Team
    Editorial Team
    2026-05-26T06:52:09+00:00Added an answer on May 26, 2026 at 6:52 am

    You are looking for the Variance. From Wikipedia:

    In probability theory and statistics, the variance is a measure of how far a set of numbers are spread out from each other. It is one of several descriptors of a probability distribution, describing how far the numbers lie from the mean

    Recipe for calculating the Variance:

    1) Calculate the Mean of your dataset

    2) For each point, calculate (X – Mean)^2

    3) Add up all those (X – Mean)^2

    4) Divide the by the number of points

    5) That is it

    The Variance gives you an idea of how “equal” your points are. A Variance of zero, means all points are equal, and then increases as the points spread out.

    Edit

    Here you may find better algorithms (more numerically stable) for calculating the variance.

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