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Related Concept Videos

Null and Alternative Hypotheses01:16

Null and Alternative Hypotheses

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The actual hypothesis testing begins by considering two hypotheses. They are termed  the null hypothesis and the alternative hypothesis. These hypotheses contain opposing viewpoints.
The null hypothesis, denoted by H0 is a statement of no difference between the variables—they are not related. This can often be considered the status quo. As  a result if you cannot accept the null, it requires some action.
The alternative hypothesis, denoted by H1 or Ha, is a claim about the...
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Types of Hypothesis Testing01:11

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There are three types of hypothesis tests: right-tailed, left-tailed, and two-tailed.
When the null and alternative hypotheses are stated, it is observed that the null hypothesis is a neutral statement against which the alternative hypothesis is tested. The alternative hypothesis is a claim that instead has a certain direction. If the null hypothesis claims that p = 0.5, the alternative hypothesis would be an opposing statement to this and can be put either p > 0.5, p < 0.5, or p...
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While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?
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Statistical Hypothesis Testing01:16

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Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
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The Availability Heuristic01:08

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A heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. Different types of heuristics are used in different types of situations, and the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):
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Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now? 
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Related Experiment Video

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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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When alternative hypotheses shape your beliefs: Context effects in probability judgments.

Xiaohong Cai1, Timothy J Pleskac1

  • 1Department of Psychology, University of Kansas, Fraser Hall, 1415 Jayhawk Blvd #340, Lawrence, KS 66044, United States of America.

Cognition
|November 15, 2022
PubMed
Summary

Subjective probability judgments are influenced by how events are described, challenging support theory's assumption of fixed support. Contextual factors, like competing or similar hypotheses, alter probability estimations.

Keywords:
Attraction effectContext effectDud-alternative effectJudgmentSimilarity effect

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Area of Science:

  • Cognitive Psychology
  • Decision Science
  • Behavioral Economics

Background:

  • Subjective probability judgments can vary based on event descriptions, not just the events themselves.
  • Tversky and Koehler's (1994) support theory posits fixed support for hypotheses but assumes support invariance.
  • This study investigates the validity of the support invariance assumption in decision-making.

Purpose of the Study:

  • To test the support invariance assumption within support theory.
  • To examine how the presence of other hypotheses influences subjective probability judgments.
  • To explore context effects in belief and preference construction.

Main Methods:

  • Three studies were conducted involving participants estimating event probabilities.
  • Participants were presented with a target hypothesis and relevant statistical information.
  • The influence of additional hypotheses (duds and resemblers) on probability judgments was analyzed.

Main Results:

  • Support for a target hypothesis is not invariant and depends on other considered hypotheses.
  • A 'dud' hypothesis increases the perceived competitiveness of the target hypothesis.
  • A 'resembler' hypothesis decreases the perceived competitiveness of the target hypothesis.

Conclusions:

  • The support invariance assumption in support theory is invalidated by context effects.
  • Belief construction, like preference construction, is susceptible to contextual influences.
  • These findings suggest a shared underlying mechanism for belief and preference formation.