Quiz: Chapter 10 - Logical Reasoning

1 What is the fundamental difference between correlation and causation?

Correlation implies causation Causation requires a demonstrated mechanism They are statistically identical Correlation is stronger evidence

2 If ice cream sales and drowning deaths both increase in summer, this demonstrates:

Ice cream causes drowning Drowning causes ice cream sales A confounding variable (heat) Statistical error

3 Which technique is MOST effective for root cause analysis?

5 Whys method Correlation coefficient Anecdotal evidence Single-factor testing

4 A study finds people who exercise daily are healthier. This proves:

Exercise causes health Healthier people exercise more A correlation exists All of the above

5 What is counterfactual reasoning?

Considering impossible scenarios Imagining alternative outcomes if circumstances differed Focusing only on present facts Rejecting all hypotheticals

6 In a causal chain A→B→C, what is B?

Root cause Mediating variable Outcome variable Confounding factor

7 Which is NECESSARY to establish causation?

Temporal precedence Perfect correlation Single case study Theoretical plausibility

8 What does a spurious correlation indicate?

A direct causal relationship A coincidental statistical relationship A proven scientific law A necessary connection

9 System thinking emphasizes:

Isolating single variables Understanding interconnected parts Linear cause-effect chains Ignoring feedback loops

10 The statement "Roosters crow causes sunrise" commits which error?

Post hoc fallacy Valid causal claim Counterfactual truth Systemic analysis

11 In root cause analysis, a fishbone diagram helps:

Show correlation coefficients Visualize potential causes by category Prove single causes Ignore systemic factors

12 Which is a STRONGER indicator of causation?

Higher correlation Dose-response relationship Anecdotal coincidence Popular belief

13 A feedback loop in systems thinking refers to:

One-way causation Outputs affecting inputs Static conditions Isolated events

14 Which is NOT a Bradford Hill criterion for causation?

Temporality Coherence Perfect correlation Biological plausibility

15 The "butterfly effect" demonstrates:

Linear determinism Nonlinear system sensitivity Spurious correlation Controlled experiments

16 Which is TRUE about confounding variables?

They simplify causal analysis They influence both cause and effect They are always measurable They prove causation

17 Double-blind experiments help establish causation by:

Eliminating placebo effects Increasing correlation Creating spurious relationships Removing all variables

18 What is the key limitation of anecdotal evidence in causal reasoning?

Lacks statistical power Too systematic Always wrong Proves theories

19 In causal mapping, an arrow represents:

Correlation only A hypothesized causal relationship Statistical significance Time intervals

20 The "third variable problem" refers to:

Missing data points Confounding factors Too few cases Overcontrol in experiments

21 Which is TRUE about necessary vs. sufficient causes?

Necessary causes always produce effects Sufficient causes guarantee effects They are identical Neither relates to causation

22 Reverse causation occurs when:

Effect precedes cause Correlation is perfect Variables are independent Systems are linear

23 What does "cum hoc ergo propter hoc" mean?

After this, therefore because of this With this, therefore because of this Correlation proves causation Sequence implies causation

24 A causal model is:

A statistical correlation A representation of cause-effect relationships An anecdotal story A single data point

25 Which is a STRONGER causal claim?

"X is associated with Y" "X increases the probability of Y" "X always causes Y" "X and Y co-occur"

26 In systems thinking, "emergence" refers to:

Properties arising from interactions Single causes Linear processes Isolated components

27 Which is TRUE about causal inference?

Requires controlled experiments Is impossible in social sciences Can use multiple evidence types Relies only on intuition

28 The "fundamental attribution error" in causality is:

Overemphasizing situational factors Overemphasizing dispositional factors Balancing all causes Ignoring correlations

29 Which method BEST establishes causation?

Case studies Randomized controlled trials Surveys Anecdotes

30 In causal networks, "d-separation" indicates:

Direct causation Conditional independence Perfect correlation Temporal sequence

31 Which is a mark of pseudoscientific causal claims?

Falsifiability Peer review Untestable mechanisms Reproducibility

32 The "Rube Goldberg effect" illustrates:

Simple causation Overly complex causal chains Perfect correlations Single-factor causes

33 Which is TRUE about causal asymmetry?

Causes and effects are interchangeable Effects cannot influence causes The causal direction matters It doesn't exist

34 A "causal fork" occurs when:

One cause has multiple effects Effects influence their causes Variables are independent Time reverses

35 Which is a criterion for causal transitivity?

If A→B and B→C, then A→C Effects precede causes No intermediate variables Perfect correlations

36 In systems thinking, "leverage points" are:

Areas where small changes create large effects Weak correlations Statistical outliers Independent variables

37 Which is TRUE about causal density?

Measures linearity Counts causal connections per variable Only applies to physics Requires perfect correlations

38 A "causal field" refers to:

Background conditions enabling causes Statistical significance Random noise Laboratory settings

39 Which is a sign of robust causal inference?

Consistency across studies Single anecdote Unfalsifiable claims Ignoring confounders

40 The "causal pie model" illustrates:

Independent causes Component causes combining to produce effects Perfect correlations Single sufficient causes

41 Which is TRUE about causal overdetermination?

Only one cause exists Multiple sufficient causes exist No causes exist Effects precede causes

42 In causal diagrams, a dotted line typically represents:

Strong causation Potential or uncertain causation Perfect correlation Temporal sequence

43 The "causal Markov condition" states:

Effects depend only on direct causes All correlations imply causation Time reversal is possible Systems are always linear

44 Which is TRUE about causal invariance?

Causes always produce identical effects Relationships hold across contexts Correlations are perfect Time doesn't matter

45 A "causal chain" is vulnerable to:

Single link failure Too many variables Perfect correlations Time reversal

46 Which is TRUE about causal feedback?

Always positive Can stabilize or destabilize systems Only occurs in machines Prevents all change

47 The "causal exclusion problem" concerns:

Too few causes Overlapping causal explanations Perfect correlations Missing data

48 Which is a characteristic of complex causal systems?

Linear relationships Predictable outcomes Emergent properties Single causes

49 A "causal interaction" occurs when:

Variables are independent The effect of one cause depends on another Correlations are perfect Time reverses

50 Which is TRUE about causal generalizations?

Always apply universally Require contextual understanding Ignore mechanisms Depend only on correlation

51 The "causal adequacy principle" states:

Causes must resemble effects Correlation is sufficient Causes must be adequate to produce effects Effects create causes

52 Which is TRUE about probabilistic causation?

Requires deterministic outcomes Allows uncertain effects Only applies to physics Ignores statistics

53 A "causal process" is distinguished by:

Temporal continuity Instantaneous change Perfect correlation Lack of mechanisms

54 The "causal closure principle" asserts:

All effects have physical causes Causes must be visible Correlation is enough Time is irrelevant

55 Which is TRUE about causal explanation?

Only one explanation exists Multiple levels may be valid Correlation suffices Mechanisms don't matter

56 A "causal context" refers to:

Laboratory conditions only Background enabling conditions Statistical significance Perfect correlations

57 The "causal parity thesis" suggests:

Causes and effects are symmetrical Only deterministic causes exist All correlations are causal Time doesn't matter

58 Which is TRUE about causal modeling?

Always proves causation Represents hypothetical relationships Requires perfect data Ignores mechanisms

59 The "causal decision theory" emphasizes:

Expected outcomes of actions Only correlations Ignoring consequences Fixed probabilities

60 A "causal structure" is:

A set of independent variables A network of cause-effect relationships A single correlation An isolated event

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Chapter 10: Logical Reasoning - Causality