Taking a real dataset — census, NSSO, a household survey, ward-level municipal data — and getting a defensible finding out of it. The analytical spine of think-tank, policy, research-fellowship and multilateral work, where the deliverable is a claim someone will argue with.
Needed across
What you will be able to answer
A colleague has run a household survey across two districts and concluded that the sanitation programme raised school attendance. What do you check before that goes into the report?
How the sample was drawn and who it left out, since a convenience sample from two districts cannot support a claim about a population; then whether the two groups differ in anything beyond the programme. Attendance and programme exposure moving together is a correlation, and the confounder — household income, or how those districts came to be selected in the first place — has to be named and handled rather than gestured at. The finding goes into the report with an interval around it and worded as an association, unless the design supports more than that.
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Course outline
Concept 1
Concept 1 · What a dataset can and cannot tell you
Concept 2
After: what-the-data-can-answer
Nominal, ordinal and interval/ratio, and how the type rules out summaries and charts before you pick one.
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Concept 3
After: variables-and-measurement
Messy phone numbers standardised with regex replacement, and the data-type problem underneath them.
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Concept 4
After: variables-and-measurement
Standard deviation and the 68-95-99.7 rule worked on real adult height data.
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Concept 5
After: describing-a-distribution
Row percentages and column percentages off the same table, answering two different questions.
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Concept 6
After: comparing-groups
A line chart used on categorical data, and the false trend it invents.
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Concept 7
After: describing-a-distribution
Simple random against stratified, and how non-response quietly biases the result.
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Concept 8
After: sampling
A 95% confidence interval interpreted correctly, with the common misreading stated and refuted.
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Concept 9
After: uncertainty-and-intervals
A two-tailed test worked through on recovery times, from null hypothesis to p-value.
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Concept 10
After: charting-a-finding
A confounder worked in full — age driving both shoe size and reading ability.
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Concept 11
After: correlation, hypothesis-testing
A multiple regression equation read out loud, coefficient by coefficient.
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11 candidates did not meet the course criteria.
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