New version of statistical analysis tool, Removes some limitations, normality assumption Options

[Garf]

Some time ago I needed an analysis of some test results and tried to use the bootstrap utility we have used for the listening tests. Unfortunately, the results coming out were bogus. I traced it down to an obscure 64-bit compatibility issue, but going through the code some things bothered me. ff123 improved my initial version significantly, but one of the things that was done was to use a normal distribution approximation for test statistics. If you consider the original version of the utility was exactly written to avoid any assumptions about normality, that's a bit sad.

So I ended up rewriting the whole thing and fixing all outstanding issues. The new version:

• Works correctly on 64-bit systems
• Removed all arbitrary limitations of number of samples, codecs, ...
• p-values are estimated through Monte Carlo resampling instead of normal distribution approximation
• Blocked and non-blocked analysis fully supported
• Comparison based on median instead of means supported
• Possible to (only) compare all samples against the first one
• Much slower because it's in Python (v2.5+ required)

This is new so it might still contain some bugs. Any feedback appreciated.

Download page

[dtrules]

• p-values are estimated through Monte Carlo resampling instead of normal distribution approximation

A quick question: why is it that the usual (binomial) p-values for n trials and k successes are calculated as (in pseudo-TeX notation):

\sum_{i = 0}^k \choose{n}{i} p^i q^{n-i}

where p is the probability of success in a Bernoulli experiment and q = 1 - p, instead of only:

\choose{n}{k} p^i q^{n-i}

If the person correctly marked k of those trials are the "correct sample" and there are \choose{n}{k} possibilities given of choosing k from a row of n experiments, why are we summing for other values of k?

[Garf]

Because we're interested in the odds that randomly picking will produce a score of k successes or more.