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Tall Stories or how a simple question doesn’t always have a simple answer

Tall Stories or how a simple question doesn’t always have a simple answer. Neil Sheldon Royal Statistical Society Centre for Statistical Education neilsheldon.net. Some people think statistics is a branch of mathematics . ... but they’re wrong. It’s more important than that .

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Tall Stories or how a simple question doesn’t always have a simple answer

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  1. Tall Storiesorhow a simple question doesn’t always have a simple answer Neil Sheldon Royal Statistical Society Centre for Statistical Education neilsheldon.net

  2. Some people think statistics is a branch of mathematics ...

  3. ... but they’re wrong.It’s more important than that ...

  4. ... it’s a life skill

  5. The purpose of statistics is understanding, not numbers

  6. Understanding statistics is understanding the world around you

  7. Understanding statistics enables you take better decisions

  8. The statistics tell a story ...

  9. ... but first you have to understand the story behind the statistics

  10. Tall Storiesor how a simple question doesn’t always have a simple answer

  11. Tall stories • How tall am I ...

  12. Tall stories • How tall am I ... • in absolute terms? What is my height in feet and inches?

  13. Tall stories • How tall am I ... • in absolute terms? What is my height in feet and inches? • in relativeterms? Am I short, or tall, or about average?

  14. Tall stories • What factors influence the answers to • the absolute question? • the relative question?

  15. Tall stories • What factors influence the answers to • the absolute question? • the relative question? • Variation, variation, variation!

  16. If only ...

  17. Men, white From the US National Health And Nutrition Examination Survey ‘NHANES III’ 1988-94

  18. Men, black

  19. Men, Hispanic

  20. Men, other

  21. Women, white

  22. Women, black

  23. Women, Hispanic

  24. Women, other

  25. But where do the data come from? And what are the implications of sampling variation? NormalSimulation.xlsx

  26. Cross-sectional and longitudinal studies Age differences in height derived from cross-sectional studies can be the result of differential secular influences among the age cohorts. To determine the magnitude of height loss that accompanies aging, longitudinal studies are required. The authors studied 2,084 men and women aged 17–94 years enrolled from 1958 to 1993 in the Baltimore Longitudinal Study of Aging, Baltimore, Maryland. On average, men's height was measured nine times during 15 years and women's height five times during 9 years. The rate of decrease in height was greater for women than for men. For both sexes, height loss began at about age 30 years and accelerated with increasing age. Cumulative height loss from age 30 to 70 years averaged about 3 cm for men and 5 cm for women; by age 80 years, it increased to 5 cm for men and 8 cm for women. Am J Epidemiol 1999;150:969-77.

  27. Longitudinal data

  28. Overlapping longitudinal data

  29. They were shorter back then ... • Judged by the height of the doorframes he built, medieval man seems to have been short by today’s standards. • But evidence gathered from 3,000 skeletons reveals that human height has varied little over the past 1,000 years.  • From the 10th century through to the 19th, the average height of adult men was 5ft 7in or 170cm - just 2in below today's average.  • Women were an average of 5ft 2in or 158cm - just over an inch shorter than today.  All the bones in the study came from the medieval St Peter's Church in Barton upon Humber, North East Lincolnshire. 

  30. ... Or were they? Based on a modest sample of skeletons from northern Europe,average heights fell from 173.4 cm in the early MiddleAges to a low of roughly 167 cm during the 17thand 18th centuries.Taking the data at face value, thisdecline of approximately 6.4 cm substantially exceedsany prolonged downturns found during industrialization in severalcountries that have been studied. Significantly, recovery tolevels achieved in the early Middle Ages was not attained untilthe early 20th century. It is plausible to link the declinein average height to climate deterioration; growing inequality;urbanization and the expansion of trade and commerce, whichfacilitated the spread of diseases; fluctuations in populationsize that impinged on nutritional status; the global spreadof diseases associated with European expansion and colonization;and conflicts or wars over state building or religion.Becauseit is reasonable to believe that greater exposure to pathogensaccompanied urbanization and industrialization, and there isevidence of climate moderation, increasing efficiency in agriculture,and greater interregional and international trade in foodstuffs,it is plausible to link the reversal of the long-term heightdecline with dietary improvements.Richard H Steckel

  31. Variation in height during the day

  32. Variation in height during the day Did you know that astronauts are up to 2 inches taller while they're in space? As soon as they come back to Earth, though, they return to their normal height. Imagine that the vertebrae in your back form a giant spring. Pushing down on the spring keeps it coiled tightly. When the force is released, the spring stretches out. In the same way, the spine elongates by up to three percent while humans travel in space. To some degree, a similar stretching of the spine happens to you every night. When you lie down, gravity isn't pushing down on your vertebrae. Measure your height carefully as soon as you get up or while you are still lying down. You will find that you're about a centimeter or two taller.

  33. Variation with wealth

  34. Mean height of Dutch adults

  35. Time series data

  36. Variation by sex Men are, on average, taller than women. But some women are taller than some men. How can we quantify this? men and women heights overlap.xls

  37. Data, data everywhere ... Human height - Wikipedia, the free encyclopedia.htm

  38. Self-reported height

  39. Genetic variation • Children of tall parents are, on average, • as tall as • taller than • shorter than their parents ... ? • Parents of tall children are, on average, • as tall as • taller than • shorter than their children ... ?

  40. Regression to the mean C = P C = P C C P P Individual heights are measured in standard deviations from male mean or female mean as appropriate. Then P is the average of father’s height and mother’s height

  41. Variation, variation, variation • A very simple question, ‘How tall am I’, raises many issues to do with variation • These issues go to the heart of many statistical concepts

  42. Variation, variation, variation • variation within groups This is ‘the usual’ concept of variation: the variability within a population or a sample is measured by the standard deviation or the inter-quartile range

  43. Variation, variation, variation • variation between groups Groups may differ from one another. Sometimes the variation between groups is more important, sometimes it is less important, than the variation within groups. (Analysis of variance treats this in fine detail.)

  44. Variation, variation, variation • variation within individuals Sometimes the attribute to be measured is not constant: • it may have a cyclical variation • it may have a trend over time • ... and it may have both

  45. Variation, variation, variation • variation over time Where an attribute is observed to vary with time, the variation may be • cross-sectional: “the older ones were like that when they were young” • longitudinal: “that’s what happens as you get older” • ... or a combination of the two

  46. Variation, variation, variation • historical variation A longer-term variation that may be quite distinct from longitudinal or cross-sectional variation

  47. Variation, variation, variation • variation in definition Any attribute being measured or counted has first to be defined. It is very common for definitions to vary from one situation to another

  48. Variation, variation, variation • sampling “error” variation A misnomer, as it is in the nature of samples to vary: it’s not a bug but a feature. We all know that samples vary, but we are often tempted to read more information into a sample than it can actually offer

  49. Variation, variation, variation • sampling bias variation There are many ways in which a non-random sample can be unrepresentative. Opportunity sampling – measuring or counting whatever is at hand – may be the most common and the most dangerous

  50. Variation, variation, variation • self-reporting variation Lacks objectivity and so can be deeply misleading. It’s like anecdotal evidence on a large scale

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