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Statistical Shenanigans: From Sweet Peas to Nobel Prizes,   how much that we take for granted ain't necessarily so  Danny Dorling Royal Statistical Society  Annual Conference, Brighton 17 th  September 2010 12:15pm - 1:35pm Plenary 6 - Significance Auditorium 2 - Hewison Hall Watch multimedia versions of the Sasi slideshows at  http://sasi.group.shef.ac.uk/presentations/
Objectives ,[object Object]
Method/Models ,[object Object]
Results and Conclusions ,[object Object],[object Object]
Politically innumerate? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Think about education stats ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Figure 1: Children by student proficiency in science in the Netherlands, according to the OECD, 2006 (%) Is this how children in the Netherlands really are?
Figure 2: Distribution of children by proficiency in science, according to the OECD, 2006 (%) Children   … .maybe this is all b- b- b- baloney (mustn’t use a rude word now, not if we are well-educated). Look at the shape of those curves ……. They are all very similar aren’t they?
Figure 3: School-leaving age (years) and university entry (%), Britain, 1876-2013   Note - school leaving age in years, left hand axis and line marked by X's; university entry % by age 30, right hand axis and line marked by filled black circles – for sources see “Statistical clues to social injustice” Radical Statistics Journal, v102. Haven’t we Been getting Cleverer?
What could be different? ,[object Object],And how does what we still do now appear so often to replicate mistakes that we made in the past?
Figure 4: Geographical distribution of paupers, England and Wales, 1891 Source: Figure redrawn from the original. Pearson, K (1895) ‘Contributions to the mathematical theory of evolution – II. Skew variation in homogeneous material’, Philosophical transactions of the Royal Society of London, Series A, Mathematical, vol 186, pp 343-414, Figure 17, plate 13) Too good to be true?
A couple of hypothesis ,[object Object],[object Object]
Don’t the little dots form a pleasing pattern? - maybe a little too pleasing. I just point this out to suggest someone checks. Galtons’ 1877 graph:  Source: Magnello, E. and B. V. Loon (2009). Introducing Statistics. London, Icon Books. (Page 123)
Floating a boat If someone finds that Galton’s famous sweet pea graph was a little too good to be true this does not mean that sweet peas did not behave in this way. It is just an example of what was then normal and what, in a slightly tempered form, is still normal amongst many statisticians: to get a little carried away with underlying theories that everything is normally distributed and, if that is not found to be the case then fit the data to such a curve to make it ‘normal’.
Here’s one I made up earlier ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Figure 6: Distribution of income showing inequality (US$), worldwide, 2000 Source: Figures (in purchase power parity, US$) derived from estimates by Angus Maddison, from a version produced in spreadsheets given in ww.worldmapper.org, based in turn on UNDP income inequality estimates for each country. See Dorling, D., 2010, Injustice:
How we got today’s inequalities Figure 7: Real growth per  decade  in GDP (%), per person, by continent, 1955–2001 . The log normal distribution we see today was due to 1980s divergence Source: as Figure 6
But how well off are the rich? Figure 8: Households’ ability to get by on their income in Britain, 1984–2004  Source: Derived from ONS (2006) Social Trends (No 36), London: Palgrave Macmillan, table 5.15, p 78, mean of  1984, 1994 and 2004 surveys.
And how unusual are our times? Figure 9: Share of all income received by the richest 1% in Britain, 1918–2005  Note: Lower line is post-tax share . Source: Atkinson, A.B. (2003) ‘Top incomes in the United Kingdom over the twentieth century’, Nuffield College Working Papers, Oxford (http://ideas.repec.org/p/nuf/esohwp/_043.html), figures 2 and 3; from 1922 to 1935 the 0.1% rate was used to estimate the 1% when the 1% rate was missing, and for 2005 the data source was Brewer, M., Sibieta, L. and Wren-Lewis, L. (2008) Racing away? Income inequality and the evolution of high incomes, London: Institute for Fiscal Studies, p 11; the final post-tax rate of 12.9% is derived from 8.6%+4.3%, the pre-tax rate scaled from 2001.
And is recession really over? Figure 10: The crash: US mortgage debt, 1977–2009 (% change and US$ billion) Source: US Federal Reserve: Debt growth, borrowing and debt outstanding tables ( www.federalreserve.gov/releases/Z1/Current/ ) Right-hand axis, net US$ billion additional borrowed - Left-hand axis: percentage change in that amount.
So what happens next? ,[object Object]
Peace Prizes – and a final puzzle So are times changing? For source data:  See: http://www.sasi.group.shef.ac.uk/injustice / Figure 11: Female Nobel laureates (%), by decade, worldwide, 1901–2009 Why in one decade was not a single women awarded a prize? Claim it was very unlikely to have happened by chance, just as the 2009 prize distribution was extremely unlikely
Conclusion ,[object Object],[object Object],[object Object],[object Object],[object Object],Watch multimedia versions of the Sasi slideshows at  http://sasi.group.shef.ac.uk/presentations/

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Statistical Shenanigans: From Sweet Peas to Nobel Prizes, how much that we take for granted ain't necessarily so

  • 1. Statistical Shenanigans: From Sweet Peas to Nobel Prizes, how much that we take for granted ain't necessarily so Danny Dorling Royal Statistical Society Annual Conference, Brighton 17 th September 2010 12:15pm - 1:35pm Plenary 6 - Significance Auditorium 2 - Hewison Hall Watch multimedia versions of the Sasi slideshows at http://sasi.group.shef.ac.uk/presentations/
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  • 7. Figure 1: Children by student proficiency in science in the Netherlands, according to the OECD, 2006 (%) Is this how children in the Netherlands really are?
  • 8. Figure 2: Distribution of children by proficiency in science, according to the OECD, 2006 (%) Children … .maybe this is all b- b- b- baloney (mustn’t use a rude word now, not if we are well-educated). Look at the shape of those curves ……. They are all very similar aren’t they?
  • 9. Figure 3: School-leaving age (years) and university entry (%), Britain, 1876-2013 Note - school leaving age in years, left hand axis and line marked by X's; university entry % by age 30, right hand axis and line marked by filled black circles – for sources see “Statistical clues to social injustice” Radical Statistics Journal, v102. Haven’t we Been getting Cleverer?
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  • 11. Figure 4: Geographical distribution of paupers, England and Wales, 1891 Source: Figure redrawn from the original. Pearson, K (1895) ‘Contributions to the mathematical theory of evolution – II. Skew variation in homogeneous material’, Philosophical transactions of the Royal Society of London, Series A, Mathematical, vol 186, pp 343-414, Figure 17, plate 13) Too good to be true?
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  • 13. Don’t the little dots form a pleasing pattern? - maybe a little too pleasing. I just point this out to suggest someone checks. Galtons’ 1877 graph: Source: Magnello, E. and B. V. Loon (2009). Introducing Statistics. London, Icon Books. (Page 123)
  • 14. Floating a boat If someone finds that Galton’s famous sweet pea graph was a little too good to be true this does not mean that sweet peas did not behave in this way. It is just an example of what was then normal and what, in a slightly tempered form, is still normal amongst many statisticians: to get a little carried away with underlying theories that everything is normally distributed and, if that is not found to be the case then fit the data to such a curve to make it ‘normal’.
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  • 16. Figure 6: Distribution of income showing inequality (US$), worldwide, 2000 Source: Figures (in purchase power parity, US$) derived from estimates by Angus Maddison, from a version produced in spreadsheets given in ww.worldmapper.org, based in turn on UNDP income inequality estimates for each country. See Dorling, D., 2010, Injustice:
  • 17. How we got today’s inequalities Figure 7: Real growth per decade in GDP (%), per person, by continent, 1955–2001 . The log normal distribution we see today was due to 1980s divergence Source: as Figure 6
  • 18. But how well off are the rich? Figure 8: Households’ ability to get by on their income in Britain, 1984–2004 Source: Derived from ONS (2006) Social Trends (No 36), London: Palgrave Macmillan, table 5.15, p 78, mean of 1984, 1994 and 2004 surveys.
  • 19. And how unusual are our times? Figure 9: Share of all income received by the richest 1% in Britain, 1918–2005 Note: Lower line is post-tax share . Source: Atkinson, A.B. (2003) ‘Top incomes in the United Kingdom over the twentieth century’, Nuffield College Working Papers, Oxford (http://ideas.repec.org/p/nuf/esohwp/_043.html), figures 2 and 3; from 1922 to 1935 the 0.1% rate was used to estimate the 1% when the 1% rate was missing, and for 2005 the data source was Brewer, M., Sibieta, L. and Wren-Lewis, L. (2008) Racing away? Income inequality and the evolution of high incomes, London: Institute for Fiscal Studies, p 11; the final post-tax rate of 12.9% is derived from 8.6%+4.3%, the pre-tax rate scaled from 2001.
  • 20. And is recession really over? Figure 10: The crash: US mortgage debt, 1977–2009 (% change and US$ billion) Source: US Federal Reserve: Debt growth, borrowing and debt outstanding tables ( www.federalreserve.gov/releases/Z1/Current/ ) Right-hand axis, net US$ billion additional borrowed - Left-hand axis: percentage change in that amount.
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  • 22. Peace Prizes – and a final puzzle So are times changing? For source data: See: http://www.sasi.group.shef.ac.uk/injustice / Figure 11: Female Nobel laureates (%), by decade, worldwide, 1901–2009 Why in one decade was not a single women awarded a prize? Claim it was very unlikely to have happened by chance, just as the 2009 prize distribution was extremely unlikely
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