Standard deviation and count rate

In summary, the conversation discusses the relationship between 2*sigma and 0.05*countRate in nuclear physics. While there is a 95% chance that the true mean lies within ±2sigma, it is unclear why 2*sigma is equal to 0.05*countRate. It is suggested to disregard this concept.
  • #1
Zuzana
12
1
Hello,

I watched MIT course on Nuclear physics (13. Practical Radiation Counting Experiments on ytb) and I do not understand why 2*sigma (standard deviation) = 0.05* countRate. As far as I know, integral of normal distribution from -2sigma to 2 sigma gives 95 % probability, but how can 2*sigma equals 100%-95% of count rate?

Thank you for the answer.
 
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  • #2
Hello @Zuzana,
:welcome: ##\qquad##!
As you may know, a counting rate obeys a Poisson distribution. ( e.g. sheet 17 here ). For reasonable counting rates, such a Poisson distribution is very close to a Gaussian distribution ( ibid sheet 20 ). Hence the 95%.

##\ ##
 
  • #3
u = measured mean
s = measured standard deviation

There is a 95% chance that the true mean lies in the interval
-1.96s+u to u+1.96s
 
  • #4
Hornbein said:
u = measured mean
s = measured standard deviation

There is a 95% chance that the true mean lies in the interval
-1.96s+u to u+1.96s
yes, I understand this, but I do not understand why should 2*sigma = 0.05*countRate.
 
  • #5
I don't know where you got that the standard deviation of count rate is count rate but the standard deviation for count rate r is r1/2 / t1/2 or (r / t)1/2 where t is the counting time.

Zuzana said:
and I do not understand why 2*sigma (standard deviation) = 0.05* countRate.
I don't understand this statement either. Perhaps you misinterpreted something in the video.

95% are between ±2σ meaning 5% is outside this interval or 2.5% above and 2.5% below.
 
  • #6
Zuzana said:
yes, I understand this, but I do not understand why should 2*sigma = 0.05*countRate.
I don't understand it either. I'd say you should disregard this confused concept.
 

FAQ: Standard deviation and count rate

1. What is standard deviation and how is it calculated?

Standard deviation is a measure of how spread out a set of data is from its average value. It is calculated by taking the square root of the variance, which is the average of the squared differences from the mean.

2. What does a high or low standard deviation indicate?

A high standard deviation indicates that the data points are spread out over a larger range, while a low standard deviation indicates that the data points are closer to the mean. In other words, a high standard deviation suggests that the data is more diverse or varied, while a low standard deviation suggests that the data is more consistent.

3. How is standard deviation used in scientific research?

Standard deviation is used to analyze and interpret data in scientific research. It helps to determine the reliability and consistency of the results, and also allows for comparison between different sets of data. It is often used in conjunction with other statistical measures to draw conclusions and make predictions.

4. What is count rate and how is it related to standard deviation?

Count rate is the number of counts or events recorded in a given time period. It is often used in experiments involving radioactive decay or particle detection. The standard deviation of the count rate can provide information about the precision and accuracy of the measurements, and can be used to assess the significance of any changes or differences in the data.

5. How can standard deviation and count rate be used to assess experimental error?

Standard deviation and count rate can be used to assess experimental error by comparing the expected or theoretical results to the actual results. If the standard deviation is high and the count rate is significantly different from the expected value, it may indicate that there were errors in the experiment or that the data is not reliable. However, a low standard deviation and a count rate close to the expected value suggest that the experiment was conducted accurately and the data is more reliable.

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