Seeking Help with Stats: Sample Size Guidance Needed

AI Thread Summary
Determining the appropriate sample size for customer satisfaction surveys depends on the company's size, the desired level of significance, and the specific statistics to be calculated. For a company conducting 100 installations monthly, the user seeks guidance on how many surveys to distribute to assess customer satisfaction effectively. The user is considering significance levels of either 1% or 5%, aiming for the option that results in fewer surveys. Understanding the relationship between sample size, significance level, and statistical power is crucial for accurate results. Assistance in calculating the sample size based on these parameters is requested.
Betty-boop
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Hi! I'm new to this forum. I took some stats in University but that was so long ago that I don't remember much.
I'm trying to figure out how big of a sample size I need to generate some stats for the company I work for. I'm not sure what info is needed but if anyone is interested in helping, I'd appreciate it.
 
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That depends on the size of the company, what statistic you want to determine, and what level of significance you want.
 
Ok, it's for customer satisfaction surveys. We do 100 installations per month, we want to send out surveys to see whether our customers are satisfied with our service. So I'd like to see how many surveys we should send out. Significance level could be either 1% or 5% whichever results in less surveys (I forget how that works). The statistics will be generated on a monthly bases.
 
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I was reading a Bachelor thesis on Peano Arithmetic (PA). PA has the following axioms (not including the induction schema): $$\begin{align} & (A1) ~~~~ \forall x \neg (x + 1 = 0) \nonumber \\ & (A2) ~~~~ \forall xy (x + 1 =y + 1 \to x = y) \nonumber \\ & (A3) ~~~~ \forall x (x + 0 = x) \nonumber \\ & (A4) ~~~~ \forall xy (x + (y +1) = (x + y ) + 1) \nonumber \\ & (A5) ~~~~ \forall x (x \cdot 0 = 0) \nonumber \\ & (A6) ~~~~ \forall xy (x \cdot (y + 1) = (x \cdot y) + x) \nonumber...
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