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Critical Thinking and Creative Thinking: Concepts, Biases, and Heuristics, Exams of Community Corrections

The concepts of critical thinking and creative thinking, highlighting their importance in decision-making and problem-solving. It delves into cognitive biases, such as confirmation bias and fundamental attribution error, and heuristic principles, including representativeness, availability, and anchoring and adjustment. Examples and explanations to illustrate these concepts, making it a valuable resource for understanding how our minds process information and make judgments.

Typology: Exams

2024/2025

Available from 03/03/2025

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CDASA 1 Creative And Critical Thinking Questions
And Correct Answers 2023
1. :
2. Critical Thinking: form of cognition that enables evaulative and
deterministic activities; quality of sources, identifying assumptions
3. Creative Thinking: generates new and unidentified connections to
broaden per- spectives; identifying new sources, developing new scenarios
for future outcomes
4. Mind-set: What people perceive, how they perceive it, and how they
process information
5. Mind-set influencing factors: past
experience education
cultural values
role
requirements
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CDASA 1 Creative And Critical Thinking Questions

And Correct Answers 2023

  1. Critical Thinking: form of cognition that enables evaulative and deterministic activities; quality of sources, identifying assumptions
  2. Creative Thinking: generates new and unidentified connections to broaden per- spectives; identifying new sources, developing new scenarios for future outcomes
  3. Mind-set: What people perceive, how they perceive it, and how they process information
  4. Mind-set influencing factors: past experience education cultural values role requirements

organizational norms specifics of information received

  1. Mind-set disadvantage: can color and control our perceptions--making slow to react to new information
  2. Mind-set advantage: helps keep production on track and maintain awareness during lulls in major crisis
  3. Principles of Perception: 1. we tend to perceive what we expect to perceive
  4. mind-sets tend to be quick to form, resistant to change
  5. new information is assimilated into existing images
  6. initial exposure to blurred or ambiguous stimuli interferes with accurate information
  7. Cognitive Bias: 1. Caused by limitations where mind employs simplifying strate- gies to ease mental processing
  8. Not caused by emotional/intellectual predisposition, subconscious
  9. Mental error that is consistent and predictable
  10. List of Cognitive Biases: Fundamental Attribution
  1. Cognitive bias--Belief bias: evaluation of logical strength of argument is biased by belief in truth/falsity of conclusion
  2. Cognitive bias--Framing: using too narrow approach and description of the situation or issue
  3. Cognitive bias--Hindsight bias: the "I knew it all along" effect; inclination to see past events as being predictable
  4. Primary Heuristic Principles: Representativeness Availability Anchoring and Adjustment
  5. Heuristic Principle #1--Representativeness: probabilities are evaluated by the degree to which 'A' is representative of 'B'; analysis of 'a' is influenced by similarities to 'b' based on stereotypes; perceive most likely outcome based on similarities; results in serious error in judgement
  6. Fallacies associated with Heuristic Principle #1-Representativeness: In- sensitivity to Prior Probability of Outcomes

Insensitivity to Sample Size Misconceptions of Chance Insensitivity to Predictability Illusion of Validity Misconception of Regression

  1. Fallacies of Representativeness--Insensitivity to Prior Probability of Out- comes: occurs when prior probabilities of outcome are neglected; tendency to favor the 'most similar' outcome to issue; base-rate frequency must be considered
  2. Fallacies of Representativeness--Insensitivity of Sample Size: occurs when a judged probability of sample statistic is made independent of sample size; judges likelihood of sample result on similarity of result to population, NOT the sample size
  3. Fallacies of Representativeness--Misconceptions of Chance: people tend to expect that a sequence of events generated randomly will represent character- istics of the process; coin toss example & gambler's fallacy
  1. Fallacies of Representativeness--Misconceptions of Regression: variations will normally regress toward mean; failure to understand regression leads to overes- timation of actions to produce 'course correction'; providing positive/negative stimuli doesn't impact whether situation will improve or deteriorate
  2. Biases associated with Heuristic Principle #2--Availability: Biases due to Retrievability of Instances Biases due to Effectiveness of a Search Set Biases of Imagineability Illusory Correlation
  3. Heuristic Principle #2--Availability: People assess the frequency of a class or probability of an event by ease with which instances or occurrences can be brought to mind
  4. Biases of Availability--Retrievability of Instances: when size of class is judged by availability of instances; instances easily retrieved will

appear more numerous than equal frequency, less memorable events

  1. Biases of Availability--Effectiveness of Search Set: Ease to which items are retrieved from memory tend to have strongest influence; easier to remember words that start with 'r' than have 'r' as third letter
  2. Biases of Availability--Imagineability: when instances of frequency can't be recalled from memory, but generated from a rule; plays important role in evaluating real-life probabilities
  3. Biases of Availability--Illusory Correlation: error in judgement of frequency with which two events co-occur; resistant to contradictory data
  4. Heuristic Principle #3--Adjustment and Anchoring: Occurs when people make estimates starting from an initial value that is adjusted to yield the final answer; most adjustments are insufficient
  5. Biases associated with Heuristic #3--Adjustment & Anchoring: Insufficient Adjustment Biases in Evaluation of Conjunctive/Disjunctive Events Anchoring in Assessment of Subjective
  1. Biases of Adjustment and Anchoring--Anchoring in Assessment of Sub- jective Probability Distributions: When analysts express overly narrow confi- dence intervals when not justified by assessed quantity of knowledge; 'judges' should be calibrated through scoring rules