Learning objectives
- Write a feasible observing or archive proposal with an auditable analysis plan.
- Explain hypothesis, sample, method, power, risks, schedule, and data management with the formal object.
- Separate evidence, transformation, inference, and limitation.
Terms Developed in Context
These terms form the lecture's reasoning chain from observable through model to a bounded physical conclusion.
- Hypothesis
A testable statement linking a proposed mechanism or relation to an observable outcome. In this lecture it supports the evidence-to-model chain for hypothesis, sample, method, power, risks, schedule, and data management.
Use and relationship: Read it together with Data-management plan and Sample design. The governing relation \(utility=expected information/(time+risk)\) shows where the concept enters quantitatively; the student should state whether its value is measured, calibrated, adopted, or inferred.
Boundary: Compression should remove redundancy, not validity-defining caveats. This caveat prevents the term from being treated as a unique explanation or a context-free number.
- Sample design
The rules defining targets, comparison groups, size, and selection effects. In this lecture it supports the evidence-to-model chain for hypothesis, sample, method, power, risks, schedule, and data management.
Use and relationship: Read it together with Hypothesis and Statistical power. The governing relation \(utility=expected information/(time+risk)\) shows where the concept enters quantitatively; the student should state whether its value is measured, calibrated, adopted, or inferred.
Boundary: Compression should remove redundancy, not validity-defining caveats. This caveat prevents the term from being treated as a unique explanation or a context-free number.
- Statistical power
The probability that a planned test detects an effect of specified size when it is present. In this lecture it supports the evidence-to-model chain for hypothesis, sample, method, power, risks, schedule, and data management.
Use and relationship: Read it together with Sample design and Risk register. The governing relation \(utility=expected information/(time+risk)\) shows where the concept enters quantitatively; the student should state whether its value is measured, calibrated, adopted, or inferred.
Boundary: Compression should remove redundancy, not validity-defining caveats. This caveat prevents the term from being treated as a unique explanation or a context-free number.
- Risk register
A ranked record of technical or scientific failure modes, consequences, and mitigations. In this lecture it supports the evidence-to-model chain for hypothesis, sample, method, power, risks, schedule, and data management.
Use and relationship: Read it together with Statistical power and Data-management plan. The governing relation \(utility=expected information/(time+risk)\) shows where the concept enters quantitatively; the student should state whether its value is measured, calibrated, adopted, or inferred.
Boundary: Compression should remove redundancy, not validity-defining caveats. This caveat prevents the term from being treated as a unique explanation or a context-free number.
- Data-management plan
A plan for formats, metadata, access, preservation, sharing, and reproducible analysis products. In this lecture it supports the evidence-to-model chain for hypothesis, sample, method, power, risks, schedule, and data management.
Use and relationship: Read it together with Risk register and Hypothesis. The governing relation \(utility=expected information/(time+risk)\) shows where the concept enters quantitatively; the student should state whether its value is measured, calibrated, adopted, or inferred.
Boundary: Compression should remove redundancy, not validity-defining caveats. This caveat prevents the term from being treated as a unique explanation or a context-free number.
Method in context
A strong proposal makes failure informative by stating what result would change the conclusion. Ask what a second researcher needs to reproduce the claim: raw input, processing state, software or database version, and inferential step.
Derivation and assumptions
Define every symbol and explain the failure mode. A method becomes scientific when assumptions are visible enough to be tested.
Worked example
A 90-second talk needs one question, one evidence figure, one uncertainty statement, and one consequence.
Compression should remove redundancy, not validity-defining caveats. Require an independent recomputation and one perturbation.
Misconceptions and study guidance
Do not confuse a data service with a measurement, a catalog identifier with physical identity, a formal error with total uncertainty, or a polished figure with a validated inference. Reconstruct the evidence chain before interpreting.
Verified reading
MAST data policies; Gaia credit instructions; NASA Exoplanet Archive; AAS Code of Ethics.
Use the exact documentation anchor in the reference log and record any access mismatch.