Lindley's list
below is the only loosely comparable *explicit* list I know.
Of course, by reading any author and seeing what examples they employ one can extract some *
implicit short list* of what they appear to view as representative examples.
Such lists comprise the remainder of this page.

- (3) If an election were to be held tomorrow, 48% would vote Democrat.
- (3, 48) The proportion of HIV cases in the population currently exceeds 10%
- (5) A card drawn from a well-shuffled deck will be an ace.
- (6) The horse, High Street, will win the 2.30 race.
- (10,15) The flight will arrive in London tomorrow morning.
- (22,81) Shares in pharmaceutical companies will rise over the next month.
- (25) There will be a serious nuclear accident in Britain next year.
- (26) It will rain tomorrow.
- (35) Inflation next month will be 3.7%
- (35) The planting of genetically modified crops will damage the environment.
- (44) The defendant is [truly] guilty.
- (45) The addition of selenium to your diet will reduce your chance of getting cancer.
- (45) The British should reduce the amount of saturated fat in their diet.
- (46) The Princes in the Tower were murdered on the orders of Richard III.
- (46) Many eighteenth century painters used lenses and mirrors.
- (46) Mrs Anderson was Anastasia, daughter of the last Tsar of Russia.
- (47) The skull is 7 million years old and is that of a hominid.
- (48) The capital of Liberia is Monrovia.

- Jesus was the son of God.
- The sun will rise tomorrow at the time stated.

- (3) accuracy of opinion polls vs expert assessments.
- (6) sports betting.
- (7) baseball player's performance.
- (8) professional poker.
- (16) flu pandemics.
- (26) weather.
- (32) mortgage default likelihoods.
- (34) predicting business cycle/economic indicators.
- (36) terrorism.
- (45) climate change.
- (65) predicting earthquakes.
- (81) stock market, efficient market hypothesis, bubbles.
- (84) Herding, overconfidence.

- (5,6) gambling
- (9) coincidences
- (14,34) unemployment
- (15) accidents to children
- (15) accidents involving transportation
- (15) workplace accidents
- (15) accidents involving extreme sports
- (16) health effects of diet/exercise/smoking/alcohol
- (16) medical risks to child in birth and infancy
- (16,19) sex - disease and accidental pregnancy
- (16,17) risks from use of illegal drugs or abuse of prescription drugs
- (17,18) radiation
- (18) vaccination
- (18) surgery
- (18) screening for disease
- (19) risks to mother in giving birth
- (20) violence/abduction to children
- (20) crime to adults
- (24) medical expenses after retirement
- (45) climate change
- (96) asteroids

* Our prototypical examples [of probability] are artificial randomizers.
But as we start to think hard about more real-life examples, we get further and further away from the core examples.
Then our examples tend to cluster into belief-type examples, and frequency-type examples, and in the end we develop ideas
of two different kinds of probability.*

The text is accompanied by a graphic, in which the following 13 "examples" are arranged in a circle around a central entry "artificial randomizers".

- (5) Urns
- (5) Lotteries
- (5) Cards
- (5) Dice
- (9) Coincidences
- (15) Frequency of traffic accidents
- (26) The weather
- (41) Radioactive decay
- (44) Guilt of accused criminals
- (51) The single case
- (61) Probability of a live birth being female
- (74) Telephone waiting times
- (96) Extinction of the dinosaurs

(xxx move to a taxonomy page). Short lists of examples are appropriate and indispensible for illustrating a distinction implied by a definition (e.g. qualitative vs quantitative variable) or a distinction that is uncontroversially substantive and useful (e.g. marine mammal vs fish). But if you wish to put forward an argument that some distinction is substantive and useful then a short list of iconic examples on both sides is not at all convincing. You need to xxx show that "most" examples can be decisively put on one side or the other, and that xxx not too unbalanced. xxx need long list xxx need list not chosen by you!

- The statements of the theory of probability cannot be understood correctly if the word `probability' is used in the meaning of everyday speech; they hold only for a definite, artificially limited rational concept of probability.
- This rational concept of probability acquires a precise meaning only if the collective to which it is applied is defined exactly in each case. A collective is a mass phenomenon or repetitive event that satisfies certain conditions; generally speaking, it consists of a sequence of observations which can be continued indefinitely.
- The probability of an attribute (a result of observations) within a collective is the limiting value of the relative frequency with which this attribute recurs in the indefinitely prolonged sequence of observations. This limiting value is not affected by any place selection applied to the sequence.

- numerical probabilities only make sense in the context of repeatable chance experiments.

- (4) Drug tests
- (5) Dice,
- (5) Coin tosses,
- (5) Lotteries,
- (5) Urns
- (23) Life expectancy,
- (23) Life insurance
- (41) Quantum theory,
- (41) Radioactivity
- (53) Shooting at a target (as prototype of repeated chance experiment)
- (54) The "theory of errors"
- (61) Population genetics,
- (61) Sex ratio at birth
- (64) Statistical physics,
- (64) Brownian motion.

- (3) In many statistical contexts, experimenters have to select a representative sample of a population
- (4) In Fisher's famous thought experiment, we suppose a woman claims to be able to taste whether milk was added to the empty cup or to the tea
- (5) gaming devices such as coins and dice
- (8) Another good example of random behaviour occurs in game theory.
- (41) quantum mechanical systems.
- (61) the Hardy-Weinberg Law
- (62) A further example is provided by the concept of random mutation in classical evolutionary theory.
- (65) Examples from "chaos theory" have been particularly prominent recently
- (72) Therefore, these models include a random noise factor: random alterations of the signal with a certain probability distribution.
- (87) processes that are modelled by probabilistic state transitions ... [e.g.] the way that present and future states of the weather are related
- (???) randomness of the rainfall input is important in explaining the robust structure of the dynamics of soil moisture.

The examples in the four books above were rather easy to fit into our contexts. I suspect this is because the authors actually started by thinking of a "context" then invented an example. When you take actual specific real-world examples (like the final one above) it becomes harder to fit into prespecified contexts. Unsurprisingly!

- (3) Opinion polls
- (5) Gambling; lotteries, cards, dice, roulette
- (7) Baseball statistics, hot hands
- (14) Success in individual careers (Bill Gates etc)
- (18) False positives in medical tests
- (23) Life expectancy, insurance
- (25) Unpredictable reliability of human artifacts (Three Mile Island)
- (35) Unpredictability in geopolitics (Pearl Harbor)
- (44) DNA profiling, reliability of eyewitnesses
- (54) Measurement error
- (64) Brownian motion
- (83) Poor intuition about chance (probability matching, imputing pattern to randomness)

**Analaysis.** xxx

- (5) poker
- (6) sports gambling
- (9) coincidences and rare events
- (26) weather forecasting
- (26) avalanche prediction
- (41) quantum theory
- (44) statistics in law
- (55) frequentists vs Bayesians
- (62) chance mutations
- (65) deterministic chaos
- (73) random number generators
- (93) evolution of complex life

xxx Rosenthal lightning book.