Recently I have a statistics question in mind - I know this is a bioinformatics QA site, so please bear with me.
Let's say we are repeatedly tossing a fair coin, and we know number of heads and tails should be roughly equal. When we see a result like 10 heads and 10 tails for a total of 20 tosses, we believe the results and are inclined to believe the coin is fair.
Well when you see a result like 10000 heads and 10000 tails for a total of 20000 tosses, I actually would question the validity of the result (did the experimenter fake the data), as I know this is more unlikely than, say a result of 10093 heads and 9907 tails.
What is the statistical argument behind my intuition?
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Haibao Tang
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This question would be better suited for the statistics stackexchange, Cross Validated. Since it's not bioinformatics-related, I'm closing this up.
This question would be better suited for the statistics stackexchange, Cross Validated (http://stats.stackexchange.com/). Since it's not bioinformatics-related, I'm closing this up.
In case google runs across this later, the question was reasked here.
Well, maybe you had a biological application in mind? This question is relevant for analysis of count data as found in RNA seq. unfortunately the acepted answer on stats. is misleading at best and shows that even statisticians can be tricked by probabilities. What you are looking for is: 'bernoulli-trial' and 'binomial distribution'. The only possible answer is: there is no way of telling (statistically) that the author cheated! If you wish rephrase your question into a bioinformatics context (e.g RNA-seq). I will re-open it.