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Data Lab9-12 · Data Lab: Conjecture and justification challenge

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Structured investigationData Science

Data Lab: Conjecture and justification challenge

Data Lab focus: coverage, selection bias, and defensible data claims. A conjecture fits familiar cases but its domain is not yet justified.

What representation and justification make a Grade 9 claim about coverage, selection bias, and defensible data claims valid within its stated domain?

CourseData Science

RigorOn-level

Performance artifactReproducible data-analysis brief

Evidence minimum2 evidence forms

Connect a table, graph, or diagram to a symbolic relationship and state its domain.

dataset
distribution
variability
representation

1Conjecture
2Represent
3Test
4Analyze assumptions
5Revise
6Justify
7Generalize

Model

Representations, assumptions, and quantities

Test 3 cases using a visual model, table or graph, and symbolic rule. Record assumptions and a boundary value.

Represent: Data Lab visual model, table or graph, symbolic rule
Justify: Connect equivalent representations and justify each inference about Use Data Lab to investigate coverage, selection bias, and defensible data claims and produce a defensible data & analysis explanation, design, interpretation, or decision.
Counterexample: A universal claim must survive a boundary case or be revised to state its domain.
Assumptions: Name the domain, parameter constraints, and whether a computed case establishes or merely illustrates the claim.

Sample coverage48%Selection bias26%

Select the conjecture that can be tested across representations and boundary cases.

Evidence

Cases and counterexample check

Commit a falsifiable model and run the repeated trials to generate the dataset.

Reproducible data-analysis brief

linked representations
valid computation
assumption or counterexample check
generalized justification

Original World tools

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