R Code Finding Stanovich

x= sample(x = -1000:1000, size  = 500)

y= sample(x = -1000:1000, size  = 500)

xpos = sample(x = 0:1000, size  = 250)

ypos = sample(x = 0:1000, size  = 250)

s = c()

t = c()

for (q in 1:1000) {

  x= sample(x = -1000:1000, size  = 500)

  y= sample(x = -1000:1000, size  = 500)

  xpos = sample(x = 0:1000, size  = 250)

  ypos = sample(x = 0:1000, size  = 250)

  

  s[q] = c(summary(lm(x~y))$coefficients[1,4])

  t[q] = c(summary(lm(xpos~ypos))$coefficients[1,4])

}

summary(s)

summary(t)

e= sample(x = 0:1000, size  = 500)

f= sample(x = 0:1000, size  = 500)

epos = sample(x = 0:500, size  = 250)

fpos = sample(x = 0:500, size  = 250)

k = c()

h = c()

for (q in 1:1000) {

  e= sample(x = 0:1000, size  = 250)

  f= sample(x = 0:1000, size  = 250)

  epos = sample(x = 0:500, size  = 125)

  fpos = sample(x = 0:500, size  = 125)

  

  k[q] = c(summary(lm(e~f))$coefficients[1,4])

  h[q] = c(summary(lm(epos~fpos))$coefficients[1,4])

}

summary(k)

summary(h)

Summary of findings: splitting the data into a half-sized category with a positive/negative split creates a nearly immeasurable difference where the normal data reveals no relationship, while the split data "reveals" a statistical significance strong enough to be considered a cosmic or particle physics discovery. With just the positive data, both come out as "significant" but the significance gets cut in half with the split data.