Use of σ in quoting measurement accuracy

In summary: I'm still not sure I understand. How do you construct this hypothetical ensemble? And are you saying that each measurement in the ensemble is a single number that differs from the others only *due to* random experimental errors? Most importantly, HOW does this apply to the examples above?In summary, σ is the standard deviation of a hypothetical ensemble of measurements of the same quantity, which differ only in random experimental errors, and are assumed to be distributed according to a Gaussian probability distribution. This is more like 99.99994% for \pm 5 \sigma.
  • #1
cepheid
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I know that σ is the symbol typically used for standard deviation, but what does the use of σ mean in these contexts?

ex. 1: "[our fancy new instrument] ... allows for the identification of > 500 sources at greater than 10-sigma"

ex. 2: "These estimates assume ... a 1σ polarization uncertainty P = 1%.

Please note that if the symbol doesn't show up for you, it is supposed to be the letter sigma.
 
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  • #2
It's the standard deviation of a hypothetical ensemble of measurements of the same quantity, which differ only in random experimental errors, and are assumed to be distributed according to a Gaussian probability distribution.

An interval of [itex]\pm \sigma[/itex] around the ideal mean value contains about 63% of the hypothetical ensemble, and [itex]\pm 5 \sigma[/itex] gets you up to about 95%, if I remember correctly.
 
  • #3
Indeed, so in terms of manufacturing defects, one sigma corresponds to the equivalent of 690,000 parts per million failures, four sigma 6,210 ppm, and six sigma 3.4 ppm.
 
  • #4
jtbell said:
An interval of [itex]\pm \sigma[/itex] around the ideal mean value contains about 63% of the hypothetical ensemble, and [itex]\pm 5 \sigma[/itex] gets you up to about 95%, if I remember correctly.

It's more like 99.99994% for [itex]\pm 5 \sigma[/itex].

http://en.wikipedia.org/wiki/68-95-99.7_rule

CS
 
  • #5
I'm still not sure I understand. How do you construct this hypothetical ensemble? And are you saying that each measurement in the ensemble is a single number that differs from the others only *due to* random experimental errors? Most importantly, HOW does this apply to the examples above? I have another one that says:

" [The instrument] has detected > 80 sources per square degree (at 5-sigma)." What does this mean? Or does this have nothing to do with the specific number 80 and more to do with the definition of what a source is as compared to the background (this is in the context of submillimetre astronomy)?
 
  • #6
cepheid said:
ex. 1: "[our fancy new instrument] ... allows for the identification of > 500 sources at greater than 10-sigma"

In this example I believe the statement means that your fancy new instrument will detect source outliers up to 10 sigma from the mean (i.e. sources with a z-score of [itex]\pm 10[/itex]). Hard to say without knowing more about the application though.

CS
 
  • #7
cepheid said:
" [The instrument] has detected > 80 sources per square degree (at 5-sigma)." What does this mean? Or does this have nothing to do with the specific number 80 and more to do with the definition of what a source is as compared to the background (this is in the context of submillimetre astronomy)?

I would interpret that as meaning that instrument detected >80 sources per square degree that were within [itex] \pm 5 \sigma [/itex] of the mean. In other words the detected value, y, was within 5 sigma of the mean value. This y value was detected >80 times.

CS
 

FAQ: Use of σ in quoting measurement accuracy

1. What is σ and how is it related to measurement accuracy?

σ, also known as standard deviation, is a measure of the spread of data points around the mean value. It is used to quantify the uncertainty or error in a measurement and is closely related to measurement accuracy. A smaller σ indicates less variation and therefore higher accuracy.

2. How is σ calculated?

σ is calculated by taking the square root of the variance, which is the average of the squared differences between each data point and the mean. In other words, it is the average distance of data points from the mean value.

3. How is σ used in quoting measurement accuracy?

When reporting the accuracy of a measurement, it is common to include the value of σ along with the mean value. This indicates the range within which the true value can be expected to fall with a certain level of confidence. For example, a measurement of 10 cm with a σ of 0.1 cm would mean that the true value is likely to fall between 9.9 cm and 10.1 cm.

4. Are there any limitations to using σ for measurement accuracy?

While σ is a useful tool for quantifying uncertainty, it is important to note that it assumes a normal distribution of data. If the data is skewed or has outliers, σ may not be an accurate representation of the spread of data points. In these cases, alternative methods such as confidence intervals may be more appropriate.

5. How can σ be improved for greater measurement accuracy?

To improve σ, it is important to ensure that the measurement process is accurate and precise. This can be achieved through proper calibration of instruments, minimizing sources of error, and taking multiple measurements to reduce random error. Additionally, increasing the sample size can also lead to a more accurate estimation of σ.

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