Calculating average from probabilistic measurements?

In summary, the conversation discusses the process of calculating the average probability and error from a set of data obtained through multiple experiments. The setup involves conducting n identical trials, repeating the process m times, and obtaining a total of N trials. The probability of success for each experiment is estimated using the formula p=s/n, resulting in a distribution of n different values for p. This process is repeated to obtain a new mean and standard deviation. The question is how to combine the two distributions.
  • #1
mkbh_10
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I have got a sample size of 1000 and I calculated the mean probability of success of an event by breaking this sample size into sets of 50 and also have the standard deviation and standard error of mean of this success.

Now I repeat this process and have another set of data.

What will be the process of calculating the average of the probability and the error?
 
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  • #2
The setup is:
You have done an experiment of n(=50) identical trials which you repeat m(=20) times getting N=nm(=1000) trials.
In each experiment you have s successes, so the probability of a success in each experiment is estimated as: p=s/n or something like that?
This gives you n slightly different values for p - a "distribution" - from which you can find a mean and a standard deviation.

You do this again ... getting another mean and standard deviation from the new distribution of p values.

Now you are wondering how to combine the two distributions?
That about it?
 

1. What is the purpose of calculating the average from probabilistic measurements?

The purpose of calculating the average from probabilistic measurements is to determine a representative value that reflects the central tendency of a set of data. This can help in making informed decisions and predictions based on the data.

2. How is the average calculated from probabilistic measurements?

The average from probabilistic measurements is calculated by taking the sum of all the measurements and dividing it by the total number of measurements. This is also known as the arithmetic mean.

3. What is the difference between average and median in probabilistic measurements?

The average in probabilistic measurements is the sum of all the measurements divided by the total number of measurements, while the median is the middle value when all the measurements are arranged in ascending or descending order. In cases where there are extreme outliers, the median may be a more accurate representation of the central tendency compared to the average.

4. Can the average from probabilistic measurements be influenced by outliers?

Yes, the average from probabilistic measurements can be influenced by outliers. Outliers are extreme values that are significantly different from the rest of the data, and they can skew the average towards their direction. This is why it is important to also consider other measures of central tendency, such as the median, when dealing with data that may have outliers.

5. How can the accuracy of the average from probabilistic measurements be improved?

The accuracy of the average from probabilistic measurements can be improved by increasing the sample size. A larger sample size reduces the impact of random variations and outliers, resulting in a more reliable and accurate average. Additionally, using more precise measurement techniques and reducing human error can also improve the accuracy of the average.

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