Why trust some supposed laws of statistical sampling and convergence when you can just test them yourself? If you have a computer with R installed (also recommended: Rstudio) then you can stop dithering about whether these n=1000 studies cited in the newspapers actually resemble the truth enough, or not.

# make some people
# let's say 1e5 one-dimensional people characterised by one parameter
# like "wealth" or "health" or "support of some particular policy"
# if you want you can create subsets like "Irish" and "English"
# ... I'll leave that kind of fun to you
base <- rnorm(1e5, mean=45, sd=4)
inheritance <- exp( exp( exp( rpois(1e5, 1.1) )))
luck <- base * inheritance * rpois(1e5, 2.1)
extreme.luck <- rcauchy(1e5, location=45, scale=4)
people <- exp( base + inheritance + luck + extreme.luck )
# randomly sample the people
Nielsen <- sample( people[1:1e5], 100, replace=F )
# take some statistics of each and compare them
mean(Nielsen)
mean(people)
diff(  mean(Nielsen), mean(people)  )
# and so on
# compare histograms, compare medians, compare stdev's, compare kurtoses...

(Notice this is an economy with no geography, no choice, and no response.)

  • You could also simulate “biased sampling” by grabbing for example people[1:100] rather than sample(people[1:1e5], 100, replace=F).
  • Or to be a little biased but also a little random you could make a indexes.to.sample.from <- floor( runif( 100, min=1, max=316) ^2 ).

(Squaring will disperse the values with a bias towards the earlier. Think about that meaning of the parabola picture!)

Nice way to play around with:

  • Different functions for generating (and noising up) a bunch of sims
  • Different measures of central tendency or spread (is median better than mean? You can prove it to yourself.)
  • R. Not that we need more reasons to play around with R, but we will gladly accept them.
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