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Week 9: The Shape of the Data

3 May 20276 min read

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The shape before the name

An unusual measurement is usually followed by an ordinary one. That sentence is the whole of this week, and it is not a claim about your body, your willpower or your routine: it is a property of any two measurements that are related without being identical. Nothing acts on the second reading. There is no rebound, no correction and no debt being repaid. An extreme reading is extreme because several things went wrong at once, and several things rarely go wrong at once twice in a row, so the next reading sits closer to where most of your readings already are. Now set that beside the question of when people actually change something. Nobody takes up a sleep routine in a week they have been sleeping beautifully. Nobody rebuilds a morning on a morning that went fine. Changes start at the bottom, which means the days after a change begins are very likely to be better than the days before it, whatever the change was, and including no change at all. That is an improvement available for free to anything whatsoever, and it has been sold a great many times. It has a name, regression to the mean, and the rest of this lecture is three occasions on which it was mistaken for a result.

Three times this has already fooled somebody

Galton measured it and filed it under heredity

Francis Galton read Regression towards Mediocrity in Hereditary Stature to the Anthropological Institute and published it in 1886, and it is the first place anyone put numbers to this. He had collected family records by offering prizes for them, and Table I of that memoir cross tabulates the heights of 205 mid-parents against the heights of their 928 adult children, where a mid-parent is the average of a couple with the mother’s height first multiplied by 1.08 to put it on the same scale as the father’s. What matters is the last column, which gives the median height of the children born to each height of mid-parent, set against the mid-parent height itself.

Mid-parent height against the median height of their adult children, for the 928 adult children of 205 mid-parents in Galton's Table I.
Mid-parentChildrenMedian childBoth, on one scale of inches
72.51972.2
71.54369.9
70.56869.5
69.518368.9
68.521968.2
67.521167.6
66.57867.2
65.56666.7
64.52365.8

Heights in inches. Each row puts two points on one scale running from 64 to 73 inches: an open marker for the mid-parent height, a filled marker for the median height of their adult children, joined by a line. The dotted rule down the column is Galton's stated population mean of 68¼ inches. From Table I of Table I: Number of Adult Children of various statures born of 205 Mid-parents of various statures, Francis Galton, Regression towards Mediocrity in Hereditary Stature, Journal of the Anthropological Institute of Great Britain and Ireland, volume 15 (1886), pages 246 to 263. Public domain.

Read the two columns against each other and the whole table leans inward. The 71.5 inch mid-parents had children with a median height of 69.9, more than an inch and a half shorter than their parents, while the 64.5 inch mid-parents had children with a median of 65.8, better than an inch taller than their parents, and every row in between moves the same way by a smaller amount the closer it starts to the middle. Galton’s own summary is that the height deviate of the offspring is on average two thirds of the height deviate of its mid-parentage. One row refuses: the 72.5 inch mid-parents have a median child height of 72.2, which barely moves at all, and that row rests on 19 children of 6 mid-parents, which is its own lesson about reading a single line of any table. What Galton did next is the error the rest of this lecture is about. He treated the pattern as a fact about inheritance, a tendency of a race to fall back towards its type, and it is nothing of the kind.

The flight instructors built a training philosophy on it

The sharpest case is in Tversky and Kahneman’s “Judgment under Uncertainty: Heuristics and Biases”, published in Science in 1974, volume 185, pages 1124 to 1131, and it is worth reading in their words rather than in the retellings that came later. “In a discussion of flight training, experienced instructors noted that praise for an exceptionally smooth landing is typically followed by a poorer landing on the next try, while harsh criticism after a rough landing is usually followed by an improvement on the next try. The instructors concluded that verbal rewards are detrimental to learning, while verbal punishments are beneficial, contrary to accepted psychological doctrine.” The instructors were reporting what they saw accurately. An exceptional landing is followed by a more ordinary one, and so is a terrible one, whether or not anybody says a word in between. The authors then generalise it past flying: “the human condition is such that, by chance alone, one is most often rewarded for punishing others and most often punished for rewarding them”. Years of experience, an honest record of it, and a conclusion about how to treat people that came out of arithmetic rather than out of the cadets.

The self-improvement industry gets this for free

The instructors at least had to wait for a bad landing. Anyone selling a routine does not, because their customers arrive already at the bottom: people go looking for a new system on the week the old one visibly failed, which is the week their measurements are furthest from their own average. Whatever they buy is therefore measured from the worst point, against the days that follow it, and those days were going to be better anyway. This is not an argument that such products do nothing, and it is not an invitation to be cynical about people who say they were helped. It is an argument about one particular kind of evidence. The testimonial that opens “I was at my lowest and then I tried this” is the shape of the data being reported as the effect of the product, and no amount of sincerity in the telling can separate the two. Only a comparison can, which means something else that also started at the bottom.

You have all of this in your own records, which is where today’s lab starts: we pool the class’s week 5 records, the one run of consecutive daily numbers any of you owns, and set how far each unusually bad day sat from its own record’s middle against how far the day after it sat. The week 1 baseline is a single day, so it cannot show you what follows anything. No statistics are involved, because none are needed to see it. Then take it to your report, due next week, and to the paragraph in it that will be hardest to write. Find the day your experiment began and say where that day sat in your own distribution. If it was unremarkable, say so and move on. If it was one of your worst, then some part of your result belongs to the day rather than to what you changed, and the honest version says how much of it you cannot separate out. The report is marked on inference rather than on outcome for precisely this reason: marks for improvement would be marks for arithmetic, and this is the week that shows you why.

Teaching team