OpenCyber AcademyLesson map
Lesson 01 published. Map, example, EX001.
flowchart TD
N0["Learning outcomes"]
N1["1.1 Numbers in governance"]
N2["1.2 Percentage traps"]
N3["1.3 Precise claims"]
N4["Synthesis"]
N0 --> N1 --> N2 --> N3 --> N4
N4 --> EX["Worked example"]
EX --> Q["Exercise EX001"]
Logical drawing
flowchart LR
N0["Learning outcomes"]
N1["1.1 Numbers in governance"]
N2["1.2 Percentage traps"]
N3["1.3 Precise claims"]
N4["Synthesis"]
N0 --> N1 --> N2 --> N3 --> N4 --> Q["EX001"]
[Learning outcomes]
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[1.1 Numbers in governance]
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[1.2 Percentage traps]
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[1.3 Precise claims]
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[Synthesis]
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[Worked example] --> [Exercise EX001]
Learning outcomes
You will (1) use proportions and rates to describe governance facts; (2) avoid common errors with percentages; (3) state claims precisely.
1.1 Numbers in governance
Sofia Reyes is a bachelor's student at the fictional Brightwater College. Her tutor, Dr. Malik Haddad, uses one running case across the course: the fictional Elmfield Housing Association, a social landlord with three hundred staff and four thousand homes, which holds personal data about tenants and takes rent payments online. Elmfield's managers often describe security with numbers: most staff have done training, few laptops lack encryption. Sofia learns to turn such phrases into precise proportions with a stated numerator and denominator. A phrase like few laptops lack encryption, for example, becomes nine of one hundred and twenty laptops once both counts are known.
1.2 Percentage traps
Sofia studies common traps. A rise from two incidents to four is a hundred percent increase but only two more incidents. A fall from ten percent to five percent of overdue reviews is five percentage points, not five percent. A share without its base can mislead: ninety percent of laptops encrypted means little if the count of laptops is unknown or if the ten percent unencrypted hold the most sensitive data. She practises rewriting misleading statements into honest ones that show counts and bases.
1.3 Precise claims
Dr. Haddad asks the class to write one precise claim about Elmfield. Sofia writes: on the first of October, two hundred and seventy of three hundred staff accounts had multi-factor authentication enabled, according to the identity system report. The claim names the date, the count, the base and the source. She learns that precise claims can be checked and challenged, while vague ones cannot, which is why auditors and boards prefer them and why governance writing should use them from the start. Dr. Haddad points out that the identity system report is named as the source, so anyone doubting the two hundred and seventy can rerun it.
Worked example
Sofia rewrites vague statements as dated counts with bases and sources and avoids percentage traps.
Synthetic case. Brightwater, Elmfield and all figures are synthetic.
Errors and limits
Precise numbers can still be wrong. Sources can be out of date.
Exercises
- EX001 — Points versus percent. A fall from ten percent to five percent is how large a change?
Show marking criteria
Rubric: 3 = five percentage points;; 2 = points;; 1 = five percent;; 0 = none. - EX002 — Missing base. Why can a ninety percent figure mislead?
Show marking criteria
Rubric: 3 = the base and which items are missing are unknown;; 2 = base;; 1 = vague;; 0 = none. - EX003 — Claim parts. What four parts did Sofia's precise claim include?
Show marking criteria
Rubric: 3 = date, count, base and source;; 2 = four;; 1 = vague;; 0 = none.
Synthesis
Precise proportions with bases and sources make governance claims checkable.