Originally posted by Delirium
Just out of curiosity, are these p-values for a standard binomial distribution, or is some other crazy fancy statistical method used?

Just out of curiosity, are these p-values for a standard binomial distribution, or is some other crazy fancy statistical method used?

/* abxpval.c

 * Determines the p-value for an ABX test via simulation.

 * Runs a session (trials in a session = NUMTRIALS) and

 * repeats NUMSIMS times.  The percentage of times that

 * a session equals or exceeds the NUMCORRECT is the p-value.

 */

#include <stdio.h>

#include <math.h>



#define NUMTRIALS 16

#define NUMCORRECT 12

#define NUMSIMS  10000000L



int main(void) {

    float pval;  /* p-value of simulation */

    long i;      /* sims index */

    long j;      /* trials index */

    long c;      /* num of sessions exceeding NUMCORRECT */

    long d;      /* number correct */

    int val;     /* value of trial, 1 = correct */



    for (c=0,i=0; i<NUMSIMS; i++) {

        for (d=0,j=0; j<NUMTRIALS; j++) {

            val = rand() % 2;

            if (val == 1)

                d++;

        }

        if (d >= NUMCORRECT)

            c++;

    }

    pval = (float)c / NUMSIMS;

    printf("pval = %7.5fn", pval);

    return 0;

}

#define NUMSIMS  10000000L

pval = (float)c / NUMSIMS;

printf("pval = %7.5fn", pval);

interesting, but since i dont know cr*p about C (or math), can u explain:

from random import randrange



def one_ABX_run(num_trials, num_correct):

    """

    Performs one simulated ABX run,

    returns 0 if the trial was unsuccessful

            1 if the trial was successful

    success happens if we get more than

    num_correct trials correct, out of num_trials

    """



    c = 0

    for i in range(num_trials):

        if (randrange(2) == 1):

            c += 1

    return c >= num_correct



def calculate_pval(num_trials, num_correct, num_sims):

    """

    Estimates the probability that you will be successful

    by chance in an ABX test where you are required to get

    num_correct correct guesses out of num_trials.

    Estimation is by performing an ABX with the appropriate

    parameters num_sims times.

    """



    c = 0

    for i in range(num_sims):

        c += one_ABX_run(num_trials, num_correct)



    return float(c) / num_sims



num_trials = 16

num_correct = 12

num_sims = 10000



print "pval =",calculate_pval(num_trials, num_correct, num_sims)

from random import randrange

from operator import add



num_trials = 16

num_correct = 12

num_sims = 10000



print "pval =",float(len(filter(lambda i: reduce(add, map(lambda i: randrange(2), range(num_trials)))>=num_correct, range(num_sims))))/num_sims