G2 SEC examination repair should depend on how far an error spreads. A wrong final unit may damage one answer. A wrong denominator, source label, model assumption or intermediate value can contaminate many later steps. This spread is the error’s dependency blast radius.
This one-hundred-and-sixteenth Learner’s Guide develops dependency blast-radius control across K200 English, K210 Mathematics and K223–K225 Science. It extends Vol 0080 Dependency-Aware Repair and Vol 0112 Evidence Debt.
The blast-radius audit
When an error is found, do not erase everything. Mark the broken node, list its descendants, identify independent branches and repair in dependency order. High-fanout upstream errors deserve attention before low-fanout presentation errors.
1. wrong question number
For a wrong question number, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
2. wrong source label
For a wrong source label, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
3. wrong speaker
For a wrong speaker, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
4. wrong pronoun referent
For a wrong pronoun referent, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
5. wrong chronology anchor
For a wrong chronology anchor, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
6. wrong quantifier
For a wrong quantifier, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
7. wrong modality
For a wrong modality, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
8. wrong causal arrow
For a wrong causal arrow, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
9. wrong comparison criterion
For a wrong comparison criterion, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
10. wrong summary source point
For a wrong summary source point, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
11. wrong paragraph purpose
For a wrong paragraph purpose, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
12. wrong writing audience
For a wrong writing audience, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
13. wrong writing task bullet
For a wrong writing task bullet, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
14. wrong oral stance
For a wrong oral stance, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
15. wrong listening detail
For a wrong listening detail, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
16. wrong formula
For a wrong formula, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
17. wrong variable definition
For a wrong variable definition, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
18. wrong unit conversion
For a wrong unit conversion, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
19. wrong denominator
For a wrong denominator, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
20. wrong percentage base
For a wrong percentage base, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
21. wrong ratio orientation
For a wrong ratio orientation, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
22. wrong intermediate value
For a wrong intermediate value, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
23. wrong sign
For a wrong sign, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
24. wrong root
For a wrong root, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
25. wrong domain assumption
For a wrong domain assumption, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
26. wrong graph scale
For a wrong graph scale, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
27. wrong coordinate
For a wrong coordinate, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
28. wrong theorem
For a wrong theorem, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
29. wrong corresponding side
For a wrong corresponding side, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
30. wrong probability universe
For a wrong probability universe, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
31. wrong branch probability
For a wrong branch probability, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
32. wrong case condition
For a wrong case condition, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
33. wrong bound
For a wrong bound, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
34. wrong rounding input
For a wrong rounding input, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
35. wrong exact/decimal form
For a wrong exact/decimal form, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
36. wrong rate direction
For a wrong rate direction, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
37. wrong currency direction
For a wrong currency direction, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
38. wrong scale factor
For a wrong scale factor, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
39. wrong sequence difference
For a wrong sequence difference, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
40. wrong sequence ratio
For a wrong sequence ratio, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
41. wrong control label
For a wrong control label, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
42. wrong treatment label
For a wrong treatment label, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
43. wrong variable role
For a wrong variable role, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
44. wrong baseline
For a wrong baseline, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
45. wrong calibration
For a wrong calibration, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
46. wrong trend interval
For a wrong trend interval, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
47. wrong anomaly ownership
For a wrong anomaly ownership, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
48. wrong mechanism link
For a wrong mechanism link, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
49. wrong population scope
For a wrong population scope, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
50. wrong time scope
For a wrong time scope, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
51. wrong model assumption
For a wrong model assumption, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
52. wrong detection threshold
For a wrong detection threshold, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
53. wrong positive-test interpretation
For a wrong positive-test interpretation, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
54. wrong negative-test interpretation
For a wrong negative-test interpretation, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
55. wrong local-to-system bridge
For a wrong local-to-system bridge, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
56. wrong interaction condition
For a wrong interaction condition, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
57. wrong rate-versus-amount quantity
For a wrong rate-versus-amount quantity, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
58. wrong evidence source
For a wrong evidence source, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
59. wrong instruction layer
For a wrong instruction layer, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
60. wrong final transfer
For a wrong final transfer, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
61. wrong table row
For a wrong table row, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
62. wrong graph series
For a wrong graph series, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
63. wrong answer option
For a wrong answer option, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
64. wrong subpart inheritance
For a wrong subpart inheritance, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
65. wrong checkpoint state
For a wrong checkpoint state, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
66. wrong representation conversion
For a wrong representation conversion, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
67. wrong work-backward inverse
For a wrong work-backward inverse, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
68. wrong comparison measure
For a wrong comparison measure, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
69. wrong evidence-debt support
For a wrong evidence-debt support, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
70. upstream high-fanout error
For a upstream high-fanout error, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
71. downstream leaf error
For a downstream leaf error, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
72. independent branch error
For a independent branch error, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
73. shared input error
For a shared input error, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
74. shared assumption error
For a shared assumption error, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
75. shared denominator error
For a shared denominator error, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
76. shared unit error
For a shared unit error, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
77. shared source error
For a shared source error, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
78. shared model error
For a shared model error, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
79. repair ordering
For a repair ordering, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
80. blast-radius final rule
For a blast-radius final rule, ask which later statements, calculations or conclusions directly inherit it. Draw a small dependency tree: broken node → affected descendants. Everything outside that tree remains provisionally secure.
For practice, plant the error in a completed answer and count how many outputs change after correction. Compare this with an error at a leaf node. The purpose is to train repair priority by consequence rather than by how alarming the mistake feels.
After repair, verify the first corrected node and one downstream consequence. If both now cohere with independent constraints—source, units, bounds, controls or task—continue only through the affected branch.
Blast radius and examination time
A high-blast-radius error can justify several minutes of repair because many marks depend on it. A low-blast-radius error should usually receive a local correction. This is a practical allocation rule, not a reason to ignore small mistakes when they are cheap to fix.
Links
Use the Examination Craft hub, Vol 0070 Dependency Graphs, the relevant English, Mathematics and Science hubs, and the PSLE Learner’s Guide.
Official-source discipline
For the 2027 SEC G2 school-candidate framework, use the current SEAB G2 syllabus directory and linked subject syllabuses. Dependency blast radius is an eduKateSengkang repair framework, not an SEAB marking label.
Final rule
Repair by dependency, not panic. Find the first broken node, map what inherits it, protect independent work and verify the corrected branch before moving on.
