Wilson's 1927 paper is freely available [1]. I can't say I have brushed up on my statistics enough this decade to verify the math but the basics as I read them

Wilson's 1927 paper is freely available [1]. I can't say I have brushed up on my statistics enough this decade to verify the math but the basics as I read them are as follows: - You have a normal distribution (bell curve) of data points, in this case quality scores. - You wish to sort these points based on their respective vote totals. Any given data point has pos positive votes out of n total votes for that item. - You have a confidence interval, e.g. 95%. This confidence is expressed in terms of the bell curve, so a 95% confidence is within 1.96 standard deviations of the mean [2]. - You have a Ruby function accepting the aforementioned n, pos, and confidence variables and returning a decimal value representing the normalized confidence_interval_lower_bound, that is the quality score that our input data point has a 95% chance of meeting or exceeding. - Given a set of data points, evaluate the ci_lower_bound for each, and then sort them accordingly. The results will give you a best-guess sorting that accounts for the fact that some data points will have more votes cast for/against them than others. [1] http://www.med.mcgill.ca/epidemiology/hanley/tmp/Proportion/wilson_jasa_1927.pdf http://www.med.mcgill.ca/epidemiology/hanley/tmp/Proportion/... [2] http://en.wikipedia.org/wiki/1.96 http://en.wikipedia.org/wiki/1.96