Brexit in graphs

This is a collection of graphs showing how people voted and other interesting statistics. Some you might not have seen and others you definitely will have. I’m not going to include every graph, especially the most common ones, as you will almost certainly have seen them. Please remember to take all the polls with a pinch of salt (only a small sample of people can be asked and it may not be representative, people may have given socially desirable answers, lied, etc.). If there are any graphs you feel I have missed, please comment below and I will add them.

*Please note that while many of these graphs focus on immigration I do not think it’s the only reason (nor, by extension, fear of foreigners) people voted Leave. These graphs are meant to show people’s views and the related data.*


Before the referendum, much was made of the difference between Leave and Remain voters in their rating of the importance of immigration for their decision.2016-06-26 (5)

For all those surveyed, immigration was the most important issue but it was closely followed by the impact on the economy.

Important issues

Prior to the referendum, Leave voters were far more likely to say immigration has had a negative impact on “Britain as a whole”.2016-06-26 (6)When asked if they had been personally affected, that number dropped.personallySome people have argued that many people’s belief that there are too many immigrants in Britain is new. But British people have thought there were too many immigrants for decades. If anything, the belief that there are too many immigrants is in decline. Too many immigrantsBut surveyed after they had voted, a different story was presented. 49% of leave voters said the biggest single reason for wanting to leave the EU was “the principle that decisions about the UK should be taken in the UK”. 33% said the main reason was that leaving “offered the best chance for the UK to regain control over immigration and its own borders.” 13% said remaining would mean having no choice “about how the EU expanded its membership or its powers in the years ahead.” (Ashcroft, 2016).Leave-vs-Remain-podium-rankings-768x989This was supported by a ComRes poll conducted on the 24th which found:  “the ability of Britain to make its own laws is cited by Leave voters as the most important issue when deciding which way to vote (53%), ahead of immigration (34%).” (Comres, 2016).

But what does the data tell us about the impact of immigration?

Immigration has dramatically increased in the last decade or so. Immigration increase

Yet there appears to be no negative effect on people’s wages or employment due to increased immigration.

2016-06-26 (3)2016-06-26 (4)

Many on the Leave side have argued people voted to Leave because they had been adversely affected by immigration. If voters backed Leave because they had suffered from increased immigration, you would expect to see a correlation between voting Leave and a decrease in hourly earnings. But there is no correlation. This is evidence against (but not a refutation) of the idea people voted Leave as rational response to the negative economic effects they had suffered as a result of immigration.No correlation between wage fall and Leave vote

Education and voting patterns:

Whilst education level was the strongest correlation for voting Remain, it’s not as simple as “stupid people voted to leave”. Areas with lower education levels also reflect areas that have borne the brunt of economic hardship. They are therefore more likely to have unfavourable views of the status-quo (which has not helped them in the past) and, by extension, the Remain campaign. image

Dependency on the EU and voting patterns:imageThe graph below shows which areas were given funding by the EU over different time periods.

imageCiaran Jenkins (2016).

Income and voting patterns:

There was a negative correlation between income and remain voting; those who earned less were more likely to vote Leave.


Personality and voting patterns:

A strong correlation (r=-0.67) was found between openness (which is about being open to new experiences, “having wide interests, and being imaginative and insightful”; Srivastava, 2016) and voting Leave. Areas that had a higher concentration of people scoring highly on openness were more likely to vote Remain. 2016-06-29

Correlations between certain personality factors and voting behaviour was also found by Eric Kauffman. He analysed participant’s voting behaviour and compared it with their answers for questions that examined their authoritarianism (which is how in favour someone is of obeying authority among other things). There was almost no correlation between income but there was a correlation between voting Leave and agreeing that the death penalty is appropriate for certain crimes (for whites only).2016-06-29 (1)

Views on social issues:

For this graph, people were asked whether they thought different social issues were a force for “good” or “ill”. After that, they stated which way they voted (Leave or Remain). So it shows what percentage of people voted for Leave or Remain, given their views on different issues. It is not a poll showing how people who voted Leave or Remain view these issues. E.g. it doesn’t show 81% of Leave voters think multiculturalism is a “force for ill”. It shows that of those who think multiculturalism is a “force for ill”, 81% voted Leave. So those who hold that view were more likely to vote Leave.Cl27HTgWEAENWEcWhy so many scientists are anti-Brexit:

Britain receives a lot of funding from the EU and it is uncertain how much we would receive afterwards (though it will almost certainly decrease).

EU science funding

Voter turnout and satisfaction:

These two aren’t graphs (yet…) but they are important, especially the first. Whilst it’s true the elderly overwhelming voted Leave and the young voted Remain, the (estimated) turnout from young people was very low. So the meme of “it’s completely the old people’s fault!” isn’t totally accurate.

This was further supported by this graph which shows a correlation between age and voter turnout for different areas.


Rather unsurprisingly, Leave voters were happier than Remain voters. But it appears the vast majority of Leave voters were happy with only 1% (of those sampled) stating they were unhappy with the result. This puts the anecdotes of people voting Leave without properly thinking it through and then worrying about the consequences in context.


Despite the startling drop in the FTSE 100, it wasn’t any lower than 7 days earlier (though it got there in a more eye-catching way). As some have correctly pointed out, the FTSE 100 has recovered significantly since the initial drop. But that’s only because the pound has been devalued so it is an artificial recovery.  ftse 100

The drop in the value of the pound though was more serious when compared with the long-term trends, as it dropped to the second lowest it has ever been.

2016-06-27 (2)Compared with the Euro it’s not doing as badly, though the Euro has been struggling for years and the climb seen at the start of the graph is the result of recovering from the 2008 financial crash.2016-07-04


Ashcroft, M. (2016). How the United Kingdom voted on Thursday… and why. [online] Available at:

Burn-Murdoch, J. (2016). Brexit: voter turnout by age. [online] Available at:

ComRes. (2016). SUNDAY MIRROR POST REFERENDUM POLL. [online] Available at:

The Economist. (2016). The European experiment. [online] Available at:

Ipsos-MORI (2016). Final Referendum Poll. [online] Available at:

Ipsos-MORI (2016). Just one in five Britons say EU immigration has had a negative effect on them personally. [online] Available at:

Jenkins, C. (2016). [online] Available at:

Krueger, J. I. (2016). The Personality of Brexit Voters. [online] Available at:

Kaufmann, E. (2016). [online] Available at:

Sky Data (2016). [online] Available at:

Srivastava, S. (2016). Measuring the Big Five Personality Factors. Retrieved [2016] from

Taub, A. (2016). Making Sense of ‘Brexit’ in 4 Charts. [online] Available at:

Vox (2016). Brexit was fueled by irrational xenophobia, not real economic grievances. [online] Available at:

Notes on Paul Meehl’s “Philosophical Psychology Session” #02

These are the notes I made whilst watching the video recording of Paul Meehl’s philosophy of science lectures. This is the second episode (a list of all the videos can he found here). Please note that these posts are not designed to replace or be used instead of the actual videos (I highly recommend you watch them). They are to be read alongside to help you understand what was said. I also do not include everything that he said (just the main/most complex points).

  • Popper did not accept the verifiable criterion of meaning. Popper never said falsifiability was a criterion of meaning.
  • No experimental/quantitative for Freud (there is empirical data). Popper rejected induction completely.
  • Unscientific theories don’t give you examples of things that will show it’s wrong, just what will confirm it.
  • If P > Q (conditional) = -P (P is false) v Q (Q is true) = not true that P (P is true) v -Q (Q is false). P is sufficient for Q and Q is necessary for P.
  • If there is a semantic connection between propositions, use stronger notation I-


  • Implicative syllogism: P -> Q, P therefore Q. Valid figure. Used when a theory is predicting an event. Modus ponens.
  • P -> Q, ~P therefore ~Q. Invalid. If Nixon is honest I’ll eat my hat. Nixon isn’t honest, can’t conclude I won’t eat my hat.
  • Q -> P, P therefore Q. Invalid. All inductive reasoning is formally invalid (if the theory is true then the facts will be so. The facts are so, therefore the theory is true). Hence why all empirical reasoning is probable. Hence why it can never be proved in sense of Euclid. Used when a piece of evidence is trying to support a law.
  • P -> Q, ~Q therefore ~P. Valid. If Newton is right, then the star will be here. The star is not here, therefore Newton is wrong. Used to refute a scientific law or theory.  Modus Tollens (destructive mood). 4th figure of implicative syllogism.


  • Facts control the theory collectively over the long-haul (rather than just being dismissed after one piece of counter evidence). If the theory is robust enough/substantiated enough, allowed to roll with a piece of counter-evidence. There’s no specified point where this theoretical tenacity becomes unscientific.
  • Empirical science cannot be like formal set theory/ mathematics as it deals with probabilities.
  • Demarcation of scientific theory from non-science.
  • We don’t just state if a theory has been “slain” or not. There is some implicit hierarchy (based on evidence). Popper developed idea of corroboration. A theory is corroborated when it has been subjected to a test that hasn’t been refuted and the more risky the test (greater the chance of falsifiability as it makes more precise predictions), the better it is corroborated. A test is risky if it carves out a narrow interval out of a larger interval.
  • You need to calculate the prior probability
  • Look at theories that predict the most unlikely results.
  • Main problem with NHST as a way of evaluating theories: within parameters (set by previous evidence or common sense) you say it will fall within half this range (so 50% chance). Not impressive.
  • Salmon’s principle: principle of the damn strange coincidence (highly improbable coincidence). If absent the theory, knowing what roughly the range of values occur, I am able to pick out a number that’s a strange coincidence. But if a theory picks out that narrow number and it comes up true, then it’s strongly corroborated.


  • Salmon believes you can attach probability numbers to theories. Talked about confirmation (which Popper rejected) but they give the same numbers as Popper’s way of doing things. Salmon does this by using Bayes’ Formula.


  • Bayes’ Theorem (criticism of the Neyman, Fischerian, and Pearson): picking white marbles from urns (don’t know which urn it comes from).
  • P (prior probability of urn 1, 1/3) Q (prior probability we have picked urn 2)
  • p1= probability that I draw a white marble from urn 1 (conditional)
  • p2= probability that I draw a white marble from urn 2
  • Posterior probability/inverse probability/probability of causes: probability that if I got a white marble, I got it from urn 1
  • Pxp1                       (product=that you drew from urn 1 and got a white marble)
  • Pxp1+Qxp2          (product=that you drew from urn 1 and got a white marble PLUS you drew from urn 2 and got a white marble)- probability that you got a white marble period
  • Clinical example:
  • P1= probability of a certain symptom on having schizophrenia.
  • Prior probability- what’s the probability that someone has schizophrenia?
  • Posterior probability- what’s the probability that someone showing this Rorschach test has schizophrenia?
  • You have a certain prior on the theory, and the theory implies strongly a certain fact (p1=large, good chance of it happening). Without a theory, the Qxp2 is blank. Filled in by being “fairly small” IF you used a precise/risky test as it’s unlikely you could guess with that precision. Means that pxp1 is quite big, so the ratio is big so the theory is well supported.
  • Salmon says you want large priors (Popper says small) but both recommend risky tests that are more likely to falsify your study (due to their precise predictions)


  • Lakatos: research programs (amended theories that started out with leading programs). Kuhn=revising certain things about the theory until it has died and then you have a paradigm shift.
  • Popper says it’s far more important to predict results rather than explain an old one.


Yonce, J. L., 2016. Philosophical Psychology Seminar (1989) Videos & Audio, [online] (Last updated 05/25/2016) Available at: [Accessed on: 06/06/2016]

Notes on Paul Meehl’s “Philosophical Psychology Session” #01

These are the notes I made whilst watching the video recording of Paul Meehl’s philosophy of science lectures. This is the first episode (a list of all the videos can be found here). Please note that these posts are not designed to replace or be used instead of the actual videos (I highly recommend you watch them). They are to be read alongside to help you understand what was said. I also do not include everything that was discussed (just the main/most complex points).

  • Power of hard sciences doesn’t come from operational verbal definitions but from the tools of measurements & the mathematics.
  • A subset of the concepts must be operationally defined otherwise it doesn’t connect with the facts.
  • Methodological remarks= remark in the meta language (statements that occur in science and the relations between them, properties of statements and between statements, relations between beliefs and evidence e.g. true, false, rational, unknown, confirmed by data, fallacious, deducible, valid, probable) rather than object language (language that speaks about subject matter e.g. protons, libido, atom, g, reinforce),
  • Hans Reichenbach was wrong about induction
  • Pure observations are infected by theory (FALSE for psychology). If protocol you record is infected by theory, bad scientist e.g. Choosing to look at 1 thing rather than another just because of a theory OR falsifying data just to fit your theory.
  • Watson’s theory that learning took place in muscles (from proprioception feedback) was falsified by rats being able to negotiate a maze almost as quickly after having neural pathway that controlled proprioception feedback severed or when the maze was flooded.
  • Operationalism (we only know a concept if we can measure it & all necessary steps for demonstrating meaning or truth must be specified) sparked psychologies’ obsession with operationalising our terms (even though the harder sciences we are trying to emulate are not as rigorous with it) but Carnap suggests it is folly.
  • Logical positivism- taking things that could not be doubted by any sane person and building up from there a justification for science, and with the math and logic on top of the protocols you “coerce them” into believing in science. Urge for certainty.
  • Analyse science and rationally reconstruct (justify) it, show why a rational person should believe in science. Negative aim: liquidation of metaphysics (by creating meaning criterion).
  • A statement is cognitively meaningless if you don’t know how to verify it (either empirically or logically)- Criterion of meaning. The meaning of a sentence is the method of it’s verification, statement of affirmative meaning. A sentence’s meaning is derived from the evidence that supports it (“the meaning of a sentence is to be found entirely in the conditions under which it could be verified by some possible experience”*). Rejected because the sentence “Caesar crossed the Rubicon” means COMPLETELY different things to us and to a Centurion at Caesar’s side because we have different evidence.
  • Lots of our information comes from “authorities” (even though it’s a logical fallacy). We have to calibrate the authority and often we presume someone has done it for us so we trust it.



Mattey, G.J., 2005. Schlick on Meaning and Verification. [pdf] G.J. Mattey. Available at: <> [Accessed on: 06/06/2016]

Yonce, J. L., 2016. Philosophical Psychology Seminar (1989) Videos & Audio, [online] (Last updated 05/25/2016) Available at: [Accessed on: 06/06/2016]