Precognition paper to be published in mainstream journal

//Written by Collins Mark.

using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;

namespace Precognition_tester
{
    class Program
    {
        static void Main(string[] args)
        {
            int NumLoops = 10000;  // <== number of experiments
            int SampleSize = 50;  // <== number of participants in each experiment.

            // This represents the paper's mean DR% threshold. Used for
            // comparison of simulated mean DR% values. Should be 2.27
            // for SampleSize of 100, and 4.21% for SampleSize of 50,
            // to compare directly with paper's results.
            double DRcomparisonThreshold = 4.21;

            double memoryMean = 18.4; // <== averge number of words recalled.
            double memoryStDev = 5;   // <== standard deviation of number of words 
                                      //     recalled (I had to guess at this one)

            int ItemsPerCat = 12;
            int i;
            Random uniRand = new Random();

            // Load the category lists.
            List<string> foodList = new List<string>();
            foodList.Add("HotDogs");
            foodList.Add("Hamburgers");
            foodList.Add("Waffles");
            foodList.Add("IceCream");
            foodList.Add("Coffee");
            foodList.Add("Pizza");
            foodList.Add("Guinness");
            foodList.Add("SausageEggAndCheeseBiscuit");
            foodList.Add("Toast");
            foodList.Add("Salad");
            foodList.Add("Taco");
            foodList.Add("Steak");

            List<string> animalList = new List<string>();
            animalList.Add("Cat");
            animalList.Add("Dog");
            animalList.Add("Snake");
            animalList.Add("Whale");
            animalList.Add("Bee");
            animalList.Add("Spider");
            animalList.Add("Elephant");
            animalList.Add("Mongoose");
            animalList.Add("Wambat");
            animalList.Add("Bonobo");
            animalList.Add("Hamster");
            animalList.Add("Human");

            List<string> occupationsList = new List<string>();
            occupationsList.Add("Engineer");
            occupationsList.Add("Plumber");
            occupationsList.Add("TalkShowHost");
            occupationsList.Add("Doctor");
            occupationsList.Add("Janitor");
            occupationsList.Add("Prostitute");
            occupationsList.Add("Cook");
            occupationsList.Add("Theif");
            occupationsList.Add("Pilot");
            occupationsList.Add("Maid");
            occupationsList.Add("Nanny");
            occupationsList.Add("Bartender");

            List<string> clothesList = new List<string>();
            clothesList.Add("Shirt");
            clothesList.Add("Shoes");
            clothesList.Add("Jacket");
            clothesList.Add("Undershorts");
            clothesList.Add("Socks");
            clothesList.Add("Jeans");
            clothesList.Add("Wristwatch");
            clothesList.Add("Cap");
            clothesList.Add("Sunglasses");
            clothesList.Add("Overalls");
            clothesList.Add("LegWarmers");
            clothesList.Add("Bra");

            // Add elements to superset without clustering
            List<string> superset = new List<string>();
            for (i = 0; i < ItemsPerCat; i++)
            {
                superset.Add(foodList[i]);
                superset.Add(animalList[i]);
                superset.Add(occupationsList[i]);
                superset.Add(clothesList[i]);
            }

            mainLoop(
                NumLoops,
                SampleSize, 
                DRcomparisonThreshold,
                ItemsPerCat,
                memoryMean,
                memoryStDev,
                superset,
                foodList,
                animalList,
                occupationsList,
                clothesList,
                uniRand);
        }

        // This is the big, main loop.
        static void mainLoop(
            int NumLoops,
            int SampleSize,
            double DRcomparisonThreshold,
            int ItemsPerCat,
            double memoryMean,
            double memoryStDev,
            List<string> superset,
            List<string> foodList,
            List<string> animalList,
            List<string> occupationsList,
            List<string> clothesList,
            Random uniRand)
        {
            // Report something to the screen,
            Console.WriteLine("Simulating {0} experiments of {1} participants each", NumLoops, SampleSize);
            Console.WriteLine("...Calculating...");

            // Create list of meanDR of separate experiments.
            List<double> meanDRlist = new List<double>();

            // Initialze DR comparison counter.
            int NumDRaboveThresh = 0; // Number of DR% above comparison thesh.

            // Loop through main big loop
            for (int mainCntr = 0; mainCntr < NumLoops; mainCntr++)
            {
                // create Array of participant's DR's for a given experiment.
                List<double> DRarray = new List<double>();

                //Loop through each participant in one experiment.
                for (int participant = 0; participant < SampleSize; participant++)
                {
                    // Reset parameters.
                    int P = 0; // number of practice words recalled.
                    int C = 0; // number of control words recalled.
                    double DR = 0; // weighted differential recall (DR) score.

                    // Create recalled set.
                    List<string> recalledSet = new List<string>();
                    createRecalledSet(
                        recalledSet,
                        superset,
                        memoryMean,
                        memoryStDev,
                        uniRand);

                    // Create random practice set.
                    List<string> practiceSet = new List<string>();
                    createPracticeSet(
                        practiceSet,
                        foodList,
                        animalList,
                        occupationsList,
                        clothesList,
                        ItemsPerCat,
                        uniRand);

                    // Compare recalled count to practice set.
                    foreach (string strTemp in recalledSet)
                    {
                        if (practiceSet.Contains(strTemp))
                            P++;
                        else
                            C++;
                    }

                    // Compute weighted differential recall (DR) score
                    DR = 100.0 * (P - C) * (P + C) / 576.0;

                    // Record DR in list.
                    DRarray.Add(DR);

                    // Report output.
                    //Console.WriteLine("DR%:  {0}", DR);
                }
                // record mean DR.
                double meanDR = DRarray.Average();
                meanDRlist.Add(meanDR);

                // Update comparison counter
                if (meanDR >= DRcomparisonThreshold) NumDRaboveThresh++;

                // Report Average DR.
                //Console.WriteLine("Experiment {0}, Sample size: {1},  mean DR:  {2}", mainCntr, SampleSize, meanDR);

            }
            // Finished looping.

            // Calculate mean of meanDR
            double finalMean = meanDRlist.Average();

            // Calculate standard deviation of meanDR
            double finalStDev = 0;
            foreach (double dTemp in meanDRlist)
            {
                finalStDev += (dTemp - finalMean) * (dTemp - finalMean);
            }
            finalStDev = finalStDev / NumLoops;
            finalStDev = Math.Sqrt(finalStDev);

            // Report final results.

            Console.WriteLine(" ");
            Console.WriteLine("Participants per experiment: {0}", SampleSize);
            Console.WriteLine("Number of separate experiments: {0}", NumLoops);
            Console.WriteLine("mean of the mean DR% from all experiments: {0}",
                finalMean);
            Console.WriteLine("Standard deviation of the mean DR%: {0}", finalStDev);
            Console.WriteLine("");
            Console.WriteLine("Comparison theshold (from study): {0}", DRcomparisonThreshold);
            Console.WriteLine("Total number of meanDR above comparison threshold: {0}", NumDRaboveThresh);
            Console.WriteLine("% of meanDR above comparison threshold: {0}%", 100.0*((double)NumDRaboveThresh)/((double)NumLoops));
            Console.ReadLine();

        }

        static double Gaussrand(double unirand1, double unirand2)
        {
            return (Math.Sqrt(-2 * Math.Log(unirand1)) * Math.Cos(2 * Math.PI * unirand2));
        }

        static void createRecalledSet(List<string> recalledSet, List<string> superSet, double mean, double stdev, Random unirand)
        {
            // Determine how many words were recalled. (random)
            double unirand1 = unirand.NextDouble();
            double unirand2 = unirand.NextDouble();
            while (unirand1 == 0.0) unirand1 = unirand.NextDouble();
            while (unirand2 == 0.0) unirand2 = unirand.NextDouble();

            double gaussrand = Gaussrand(unirand1, unirand2);
            gaussrand *= stdev;
            gaussrand += mean;
            int recalledCount = (int)gaussrand;
            if (recalledCount > superSet.Count) recalledCount = superSet.Count;

            // Create temporary superset and copy elements over.
            List<string> tempSuperSet = new List<string>();
            foreach (string strTemp in superSet)
            {
                tempSuperSet.Add(strTemp);
            }

            // Randomize temporary superset.
            shuffleList(tempSuperSet, unirand);

            // Copy over first recalledCount items to recalledSet.
            for (int i = 0; i < recalledCount; i++)
            {
                recalledSet.Add(tempSuperSet[i]);
            }
        }

        static void createPracticeSet(
            List<string> practiceList,
            List<string> foodList,
            List<string> animalList,
            List<string> occupationsList,
            List<string> clothesList,
            int itemsPerCat,
            Random uniRand)
        {
            List<string> tempFoodList = new List<string>();
            List<string> tempAnimalList = new List<string>();
            List<string> tempOccupationsList = new List<string>();
            List<string> tempClothesList = new List<string>();

            // load temporary lists.
            foreach (string strTemp in foodList)
                tempFoodList.Add(strTemp);
            foreach (string strTemp in animalList)
                tempAnimalList.Add(strTemp);
            foreach (string strTemp in occupationsList)
                tempOccupationsList.Add(strTemp);
            foreach (string strTemp in clothesList)
                tempClothesList.Add(strTemp);

            // Shuffle temporary lists
            shuffleList(tempFoodList, uniRand);
            shuffleList(tempAnimalList, uniRand);
            shuffleList(tempOccupationsList, uniRand);
            shuffleList(tempClothesList, uniRand);

            // Load practice list
            for (int i = 0; i < itemsPerCat / 2; i++)
            {
                practiceList.Add(tempFoodList[i]);
                practiceList.Add(tempAnimalList[i]);
                practiceList.Add(tempOccupationsList[i]);
                practiceList.Add(tempClothesList[i]);
            }

            // Shuffle practice list
            shuffleList(practiceList, uniRand);
        }

        // method to shuffle lists.
        static void shuffleList(List<string> list, Random unirand)
        {
            List<string> shuffledList = new List<string>();
            while (list.Count() > 0)
            {
                int indexTemp = unirand.Next(list.Count());
                shuffledList.Add(list[indexTemp]);
                list.RemoveAt(indexTemp);
            }
            foreach (string strTemp in shuffledList) list.Add(strTemp);
        }
    }
}