anchoring and primacy effects

Anchoring-and-adjustment heuristic

A cognitive heuristic where one starts from an initial value (the anchor), assesses if it's too low or too high, and gradually adjusts it, often leading to insufficient adjustment.

Anchoring (Tversky & Kahneman, 1974)

Phenomenon where initial, sometimes irrelevant, numerical values (anchors) influence subsequent estimates, such as guessing the percentage of UN countries from Africa.

Anchoring (Strack & Mussweiler, 1997)

Demonstration that even implausible anchors (e.g., Gandhi's age at death being 9 or 140) can significantly influence people's subsequent estimates.

Anchoring in Judicial Decisions (Englich, Mussweiler, & Strack, 2006)

Expert judges' sentencing decisions can be influenced by irrelevant anchors, such as a randomly determined prosecutor's demand, even when told it's random.

Contrast Principle

A principle of perception where two sequential items, if fairly different, tend to be seen as more different than they actually are, often used in sales (e.g., showing expensive items first).

Selective Accessibility

A mechanism explaining anchoring, where a given anchor makes memories or information consistent with that anchor more accessible, thus influencing estimates (e.g., judging a building's height).

Primacy Effect (Asch, 1946)

In impression formation, the tendency for initial information about a person (e.g., 'intelligent') to have a disproportionately strong influence on overall judgment, leading later information to be interpreted in light of the former.

Asch's Explanation of Primacy Effects

Later characteristics of a person are interpreted in light of former characteristics, with the initial traits acting as background knowledge, shaping overall impressions.

Attribute Substitution (Kahneman & Frederick, 2002)

A revised definition of heuristic where an individual assesses a specified target attribute of a judgment object by substituting another related but more accessible property (the heuristic attribute).

Representativeness Heuristic

An example of attribute substitution where a difficult probability judgment is replaced with an easier similarity judgment.

Availability Heuristic

An example of attribute substitution where a difficult probability judgment is replaced with an easier judgment about how easily instances come to mind.

Anchoring and Attribute Substitution Distinction

According to Kahneman, anchoring-and-adjustment does not involve attribute substitution; instead, it influences judgment by temporarily raising the accessibility of a particular value of the target attribute.

Conditional Probability

The probability of an event A occurring given that another event B has already occurred, denoted as P(A|B) and often rendered as 'P of A given B'.

Bayes Theorem

A formula that describes how to update the probability of a hypothesis based on evidence: P(A|B) = P(A) * P(B|A) / P(B).

Multiplication Rule (Probability)

A rule for calculating the probability of two events A and B both occurring: P(A & B) = P(A) * P(B|A). If A and B are independent, it simplifies to P(A & B) = P(A) * P(B).

Addition Rule (Probability)

A rule for calculating the probability of either event A or event B or both occurring: P(A V B) = P(A) + P(B) – P(A & B). If A and B are mutually exclusive, it simplifies to P(A V B) = P(A) + P(B).

Null Hypothesis

In statistical testing, the hypothesis that there is no effect or no difference, which is the hypothesis being tested (and potentially nullified).

P-value

The probability, assuming the null hypothesis is true, of obtaining a result equal to or more extreme than what was observed in a study. If statistically significant (e.g., p < 0.05), the null hypothesis is usually rejected.

Inverse Fallacy (P-value)

The incorrect assumption that a p-value of 0.05 means there is a 5% probability that the null hypothesis is true, or that chance produced the observed result, confusing P(data|null) with P(null|data).

Confidence Intervals (CI)

A range of values, typically expressed as a percentage (e.g., 95% CI), implying that if an experiment were repeated many times, that percentage of the calculated intervals would contain the true population parameter.

Effect Size

A standardized measure that quantifies the strength and magnitude of a phenomenon, such as the difference between two means (e.g., Cohen's d) or the strength of a relationship between two variables (e.g., Pearson's r).

Pearson's r

A specific type of effect size that measures the strength and direction of a linear relationship between two variables, ranging from -1 (perfect negative correlation) to +1 (perfect positive correlation), with 0 indicating no linear correlation.