How to measure if a statistic is accurate?

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To assess the accuracy of a statistic derived from a limited sample size of coding techniques on 380-bit messages, it's crucial to estimate a confidence interval around the calculated statistic. Given a sample size of 10^5 to 10^6, which is significantly smaller than the total possible combinations (2^380), the confidence interval can provide insights into the reliability of the results. Mathematical theories such as the Central Limit Theorem can be applied to approximate the distribution of the statistic, allowing for better accuracy assessments. Additionally, increasing the sample size, if feasible, can enhance the precision of the estimates. Ultimately, understanding the relationship between sample size and confidence intervals is key to evaluating statistical accuracy in this context.
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I am studying the performance statistic of some coding techniques on messages of 380 bits long. I studied it by generating bit sequences randomly and test the performance on each sample. But the sample space is so large (2^380) and practically I can only randomly test about 10^5-10^6 samples, how can I know if the statistic I get is accurate enough? Is there any mathematical theory that could be used to estimate how accurate it is?
 
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Start by assuming you have 106 independent random variables. Although this is a finite number and is much smaller than 2380 (my guess), it is a sizeable sample size by any standard. You should be able to estimate a confidence interval around any statistic you have happened to calculate; the conf. int. is also a function of the sample size.
 
The standard _A " operator" maps a Null Hypothesis Ho into a decision set { Do not reject:=1 and reject :=0}. In this sense ( HA)_A , makes no sense. Since H0, HA aren't exhaustive, can we find an alternative operator, _A' , so that ( H_A)_A' makes sense? Isn't Pearson Neyman related to this? Hope I'm making sense. Edit: I was motivated by a superficial similarity of the idea with double transposition of matrices M, with ## (M^{T})^{T}=M##, and just wanted to see if it made sense to talk...

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