Calculating Mean Probability with Sampling Distributions | Sample Size 200

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The mean probability of 100 observations is .0422, and it is suggested that this value can be used as an estimate for a larger sample size of 200, with some statistical variations expected. The discussion emphasizes that while the mean will remain approximately the same regardless of sample size, the standard deviation will decrease as the sample size increases. Clarification on the type of experiment is necessary for more precise guidance. Understanding the relationship between sample size and mean probability is crucial for accurate statistical analysis. Overall, the mean probability can be extrapolated, but careful consideration of the sample context is essential.
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The mean probability of 100 observations is .0422. If you are not given the data for a sample size of 200, how do you find the mean probability of this data using the mean probability you found from .0422?

thanks
 
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Sorry, your question seems to be somewhat unclear to me. Is the .0422 the frequency of occurence of something observed? Well, then is should be the same (with some statistical variations of course) for most experiments.

If you are aiming at an entirely different point you should clarify the kind of experiment you have in mind, because most of the answers will depend on that.
 


The Mean will stay approximately the same no matter how large the size of the sample. However, the standard deviation will decrease if the sample gets larger

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thanks for answering upsidedown;
 
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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