Ivan Seeking said:
jarednjames said:
(Bold added for emphasis.)jarednjames said:
jarednjames said:
Evo said:
It's not paranormal like the Radin paper.jarednjames said:
Ok, no information here and not impressive.
Well, duh. We've known for years that it takes a while for the brain to consciously shift from one train of thought to an opposite.
Evo said:
FlexGunship said:
FlexGunship said:
Everyone keeps deleting my posts... I didn't just delete it, I sent it into oblivion.FlexGunship said:Wait... where IS my response? My well-reasoned, carefully thought out post seems to have gone decidedly AWOL.
You mean... you... edited it.. out.
Evo said:I didn't just delete it, I sent it into oblivion.
Heh, good point. "Researchers have demonstrated that they can get students to type words that they have previously remembered when asked."jarednjames said:
Well, they're saying it works in reverse. Seeing the word "ugly" before seeing a kitten delays your response on the quality of the kitten. They are claiming that after you delay your response on the quality of a kitten, then will show the word "ugly."jarednjames said:
I believe the goal was to illustrate that "although 53% might sound very close to 50%... aspirin is recommended because instead of helping 50% of people, it helps 53% of people." Therefore, we are to conclude that 53% is, indeed, a statistically significant number.Evo: Destroyer of Posts! said:
jarednjames said:
I think you might be misinterpreting the experiment, maybe. The article is ambiguous, and not well written on this point, but here is how the experiment was apparently done (I'll try to summarize it):jarednjames said:
Evo said:
collinsmark said:
//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 = 5000; // <== number of experiments
int SampleSize = 100; // <== number of participants in each experiment.
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,
ItemsPerCat,
memoryMean,
memoryStDev,
superset,
foodList,
animalList,
occupationsList,
clothesList,
uniRand);
}
// This is the big, main loop.
static void mainLoop(
int NumLoops,
int SampleSize,
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>();
// 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);
// 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.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);
}
}
}Ivan Seeking said:
This is what you posted https://www.physicsforums.com/showpost.php?p=2982604&postcount=3Ivan Seeking said:
Ivan Seeking said:
</whisper>Evo said:
Yes, if you were to flip a coin fair ten times in a single experiment, the likelihood of the coin coming up all heads on a given experiment is 1/210 or about 1 chance in 1024. If that happened on the first experimental attempt, it would be a statistical fluke. Not at all impossible but very unlikely. And if an experimenter did not know if the coin was fair or not, he might take that as positive evidence against the coin being fair, and of meriting further trials. But I'm not sure how the analogy applies to this this set of experiments though. Are you suspecting that the author of the study repeated the experiment perhaps hundreds of times, each with 50 or 100 people in each experiment (many thousands or tens of thousands of people total), and then cherry picked the best results? If so, that would be unethical manipulation of the data (and very costly toojarednjames said:
). [Edit: And besides, there are easier ways to manipulate the data.]//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);
}
}
}collinsmark said:
Egads! don't say that!jarednjames said:
Yes, I agree with that. For a particular pattern the odds are 1 in 1024 (10-toss coin experiment) for any specific pattern.
Again, for a single roll of the die you are correct.
For a single roll of the die, the probability distribution is uniform. No need to postulate malice where a simple mistake will suffice.collinsmark said:
Okay, I hadn't looked at that experiment yet, but I'll look at it now.jarednjames said:
I'm not very good at probability and statistics either. I used to know this stuff a long time ago, but I promptly forgot most of it. I had to re-teach myself much of it for this thread!
collinsmark said:
http://www.nytimes.com/2011/01/06/science/06esp.html?_r=4&hp=&pagewanted=all
I wonder why people suddenly get such sloppy logic when the subject concerns ESP.
pftest said:
Perhaps you didnt read the article, but even the quote that i used states that this was the opinion of "experts". So it isn't the reporters "personal take". I am surprised that those experts use such sloppy logic. Perhaps the reporter didnt summarise the experts views well.nismaratwork said:
pftest said:
Jack21222 said:
http://www.newyorker.com/reporting/2010/12/13/101213fa_fact_lehrer
