Spearman's rank correlation for a beginner: Sample size and Interpretation I am a computer scientist performing research, which includes calculating the Spearman rank correlation of two lists, one ranked by a human, another by a computer program.
I have the following questions:


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*I was reading this post and have become concerned with how large my sample size has to be to have a significant confidence level. 

*Also if I have understand correctly the Fisher transform gives you a confidence level after you have gotten the Spearman rank correlation. If possible I would like to know the ideal sample size before I start the experimen

*On interpreting the value given to you by the Spearman rank correlation: I have read about the null hypothesis

The general form of a null hypothesis for a Spearman correlation is:
  H0: There is no association between the two variables [in the population].

However what I want to prove is the opposite ie: that there is a very close association between the two variables. How is this done?
 A: If you are concerned about sample size and significance, good concepts to start out with include effect size and power (while on the topic of CIs, you might want to include accuracy as well)
As noted previously, 95% CI refers not to probability but to confidence; it is not the likelihood that the current CI contains the population parameter, but that out of 100 CIs 95 CIs will succeed in capturing the population parameter. The true probability remains unknown (unless you take a Bayesian approach), see Explorations in statistics: confidence intervals.
A: The formulation of the null vs. alternative hypothesis has little to do with what you "want" to find. It's how scientists (at least scientists who have a need to use frequentist statistics) address their research question. Basically, all the null says is that the relationship between these two variables is either zero or unknown. The alternate hypothesis is your suggestion/assertion that there is a relationship. 
You have a hypothesis and you are testing via significance testing of a correlation. Whatever your result, you have a piece of evidence that supports or doesn't support (or contradicts, if you use a two-tailed test) that hypothesis. 
If it doesn't, there are myriad reasons, including insufficient power, which could be the result of small samples, poor measurement, etc. The reason sample size matters is that greater samples provide greater information; greater information provides more confidence in what is observed. 
