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Chapter 5 · Connecting the Dots...
Why every answer from data raises the next question
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Onions cost thirty-five a kilo. Any questions? Now here is a year of prices in two towns, and suddenly there are six.
The idea
A single number closes a conversation; a spread of numbers opens one. That is the chapter's real closing claim, and it is a claim about how enquiry works rather than a technique to practise. Watch what the book does every time an analysis finishes: instead of a conclusion it prints the questions the analysis provoked — and those questions are always about the world outside the data. Where are these two towns? Why is one school so much taller than the other? What does a country's history have to do with its nineteen-year-olds? Data analysis is a loop, not a pipeline, and the SUMMARY ends on exactly that.
What you should be able to do
- Explain why variation in data provokes curiosity and a single flat figure does not
- Generate follow-up questions from a completed analysis, and sort them into those data could settle and those it could not
- Turn a loose wondering into a statistical question, naming what would be collected
- Recognise the chapter's own pattern: identify what is given, infer from it, then ask what to look at next
- Read a graph whose subject is deliberately withheld, and say what the shape of the data implies about it
- Plan a small data collection of your own: what to record, over what period, and how to display it
- Say why a finished analysis is a starting point rather than a result
Words to know
| Term | Definition in one line | First introduced |
|---|---|---|
| curiosity | the wanting-to-know that a spread of data provokes and a single figure does not | printed in §5.2, Part II, p.104 |
| wonder | the chapter's own verb for the questions that follow an analysis | printed in §5.2, Part II, p.104 and in §5.4, Part II, p.126 |
| explore | to follow a question further once the first answer is in | printed in §5.3, Part II, p.121 |
| infer | to draw from the data a conclusion it will support | printed in §5.3, Part II, pp.117, 119 |
| variation | the spread in a set of values, and the thing that sparks the next question | printed as variations in §5.2, Part II, p.104 |
| statistical question | a question that can only be settled by collecting data | printed in §5.1, Part II, p.98 |
| project | an extended data collection carried out by a student or a group | printed in §5.4, Part II, pp.132–133 |
| observation | something noticed in the data and worth stating aloud | printed in §5.3, Part II, pp.116, 119 |
| analysis | the working that turns collected data into something you can say | printed in §5.4, Part II, p.133, in the closing "Figure it Out" item 13; §5.1, Part II, p.98 carries only the verb, as analyse and analysing |
| research question | the question a collection is designed to answer before any data is gathered | the explanation's compound, not printed in this chapter, which says statistical question |
| enquiry cycle | the loop from question to data to answer to the next question | an added label for the pattern the chapter repeats but never names |
Where people slip up
- "An analysis ends with an answer." In this chapter it ends with a question, every single time. Count them if you like — p.104, p.118, p.121, p.126, p.128 and the last SUMMARY bullet all do the same thing.
- "Wondering is what you do before you know any mathematics." The chapter puts its densest wondering after the analysis, not before it. You need the graph to know what to ask next.
- "Every follow-up question can be answered with more data." Several of the chapter's own cannot. Asking whom price swings hurt is a question about economics and policy; the data only tells you the swings are there. Sorting questions by what could settle them is part of the skill.
- "If the book does not answer it, the question was rhetorical." Some of these are open on purpose. Leaving a question open is a legitimate ending, and an explanation that manufactures a tidy answer teaches the opposite of the section.
- "A guessing game about hidden data is a warm-up." The City 1 / City 2 exercise is doing real work: it forces the reader to read shape, symmetry and opposition out of raw numbers before any label is available to lean on.
- "Projects are optional extras." Five of the thirteen items in the closing "Figure it Out" (Part II, §5.4, pp.129–134) are collections the student runs — items 6, 9, 11, 12 and 13; the other eight print their data. That is where the loop stops being something read about and becomes something done.
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Worked answers to this chapter’s exercises · this video explains Figure it Out · 4 Q11, Figure it Out · 4 Q12, Figure it Out · 4 Q13
Transcript1,317 words
Onions cost thirty-five rupees a kilo. There. Any questions? Now here is a year of onion prices in two towns — twenty-four numbers instead of one. Something just happened. The single number closed the conversation and the table opened one. And the strange part is that thirty-five was not a lie. It is a real price. It really was paid, in one month, in both towns. In the other twenty-two months of that year it is simply wrong.
The cheapest month is seventeen and the dearest is sixty. The spread that one number hides is forty-three rupees, which is wider than the number itself. One of those is something you get told. The other is something you get to think with. So what do you want to know? Not what I want to tell you. What the table made you want to ask. Here are six questions people actually raise when they see it.
Do the seasons move the price? Where are these two towns, and how far apart are they? How much do prices differ between two shops in the same street? Do other things behave this way — rice, tomatoes, petrol? What decides the price of an onion in the first place? And whom do these swings hurt, and whom do they help? Six questions, and they are not all the same kind of question.
Sort them with one test. Could collecting data settle this? Do seasons move the price — yes. Collect several years and look. Where the towns are — yes, that is something you can go and find out. Shop to shop in one street — yes. Walk down it and write the prices down. Other goods — yes. Same table, one more column. That is four you could settle. The last two are a different animal. What decides an onion's price is a question about weather and land and trade.
And whom the swings help is a question about people's lives. Data can inform both of those. It cannot settle either. That is not a flaw in the questions. Knowing which of your questions is which is most of the skill. Take one of the four and make it properly answerable. Do seasons move the price is a wondering, not yet a question you could act on. A question you can settle names three things.
What you would collect: monthly prices. From where: these two towns, and ideally a few more. And over what stretch: several years, because one year cannot separate a season from an accident. And how you would show it, which for prices over time means a line or a column for each month. Now it is not a thought. It is something a person could go and do on Monday. Your turn, with the labels taken off.
Two rows of twelve numbers. One row for each of two places, one number for each month. I am not going to tell you what they measure. Look at them and work out what kind of thing they could be. You are allowed to add them up, compare them, or divide them by anything you like. Take your time. Those numbers are the only evidence you get. Here is what is visible already.
The first row climbs to its highest in June, and falls to its lowest in December. The second row does exactly the opposite. Highest in December, lowest in June. Each place's best month is the other place's worst. And it is not only at the two ends. From one month to the next there are eleven steps. In all eleven, when one row goes up the other goes down. Whatever these are, the two places are opposed all year long.
Now add each row up. Four thousand five hundred and twelve, against four thousand four hundred and twenty-five. Eighty-seven apart, across an entire year. Under two per cent. So the two places get almost exactly the same amount of whatever this is. They just get it at opposite times. That already rules a great deal out. Whatever this is, neither place is simply richer in it than the other. That is the clue worth holding on to. Same total, opposite shape.
One more move before I tell you. Divide each month by its own number of days, because a monthly total flatters the long months. Now the first place runs from eighteen point eight at the top down to six point zero at the bottom. Six point zero is exactly a quarter of a day. The second place runs from fifteen point one down to nine point two. The first place swings twelve point eight. The second swings five point nine.
More than twice as wide. They are hours of daylight. Hours of daylight, month by month, in two places on the same planet. And now every single thing you noticed means something. Opposite all year, because one place is north of the Equator and the other is south. Almost the same yearly total, because over a full year everywhere gets roughly the same share. The wider swing, because the first place sits further from the Equator than the second.
Eighteen hours of daylight in June and six in December is a long way north. Nothing in the table said Equator, or hemisphere, or tilt. All of that came out of the shape. You just read the tilt of a planet off two rows of numbers. And here is the thing worth noticing about what happened just then. You have your answer. Does it feel finished? If a place that far north gets eighteen hours in June, what happens further north still?
Nineteen. Twenty. Twenty-four. There are places where, in midsummer, the Sun goes round the sky and never sets. You did not learn that from the table. The table made you ask. The answer did not close anything. It pointed further north. Once you see that pattern you cannot stop seeing it. Twelve class averages from two schools, and one school is taller grade for grade, every time. Four questions come out of that, and they split two and two.
Two you could answer by going and measuring: how tall the students in your own school are, and what the average across every child of that age would be. The other two are why one school is taller, and where on earth these two schools actually are. Those questions do not get answered. Not because nobody thought of them, but because those twelve averages do not hold the answer. A question left standing is an honest place to stop.
Draw what you have been doing all the way through, and it does not come out as a line. You start with a question. You collect. You display it. You summarise it. And at the end you have a new question, which starts the whole thing again. It is a ring, not a pipeline. The tempting picture is that data goes in one end and answers come out the other.
What actually happens is that every answer hands you the next question. If your analysis ever stops handing you questions, that is worth being suspicious about. Which means you can run the ring yourself, this week, without anyone's permission. Count the letters in every classmate's name, and draw the shape that makes. Tally how often you leave the house, every day for a month. Measure everyone in your family against your own height.
Ask several people to say when they think a minute has passed, and time them. Take one page of two different books and count the words in every sentence on each. Each one is a question, a collection, a picture, and an answer that raises the next question. So here is the only thing I would like you to keep. An analysis does not end with an answer. It ends with a better question.
Where this fits
Taken from the notes each video was made from, not from the reading order — these are the ideas this one rests on and the ones that later rest on it.
Builds on
- Turning a vague comparison into a question with a data answerClass 7 · Ch 5, Connecting the Dots...
- Why one number is never enough to describe a data setClass 7 · Ch 5, Connecting the Dots...
- Telling tall tales: how a truthful graph still misleadsClass 7 · Ch 5, Connecting the Dots...
- Clustered bar graphs: comparing across categories and across timeClass 7 · Ch 5, Connecting the Dots...
Either side of this one
- The perpendicular bisector, and the equidistance property that justifies itClass 7 · Ch 6, Constructions and Tilings