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Related Concept Videos

Probability in Statistics01:14

Probability in Statistics

Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts.
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...
Probability Laws01:49

Probability Laws

Overview
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
Estimation of the Physical Quantities01:05

Estimation of the Physical Quantities

On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
Margin of Error01:27

Margin of Error

The margin of error is also called the maximum error of an estimate. The margin of error is the maximum possible or expected difference between the observed sample parameter value and the actual population parameter value. For proportion, it is the maximum difference between the value of sample proportion obtained from the data and the true value of population proportion. As the true value of the population parameter is not known, the margin of error is calculated using the sample statistic.
Unusual Results01:16

Unusual Results

Unusual results are those that have a very low chance of occurring. Unusual results can be identified using probabilities and the range rule of thumb. In problems involving probability, unusual results can be observed in 2 instances – an unusually high number of successes or an unusually low number of successes.
According to the range rule of thumb, any value above or below two standard deviations, 2σ  from the mean, μ  is considered unusual.
Maximum unusual value = μ + 2σ
Minimum unusual value...

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Related Experiment Video

Updated: Jun 27, 2026

Using the Threat Probability Task to Assess Anxiety and Fear During Uncertain and Certain Threat
11:18

Using the Threat Probability Task to Assess Anxiety and Fear During Uncertain and Certain Threat

Published on: September 12, 2014

Estimating the probability of negative events.

Adam J L Harris1, Adam Corner, Ulrike Hahn

  • 1Department of Psychology, Cardiff University, Tower Building, Park Place, Cardiff, United Kingdom. harrisaj@cardiff.ac.uk

Cognition
|November 28, 2008
PubMed
Summary

Human probability estimates for negative events are not inherently biased by severity. Participants perceived controllable negative events as more likely when their utility was extremely negative, suggesting a nuanced relationship between perceived risk and severity.

Related Experiment Videos

Last Updated: Jun 27, 2026

Using the Threat Probability Task to Assess Anxiety and Fear During Uncertain and Certain Threat
11:18

Using the Threat Probability Task to Assess Anxiety and Fear During Uncertain and Certain Threat

Published on: September 12, 2014

Area of Science:

  • Cognitive Psychology
  • Decision Science
  • Behavioral Economics

Background:

  • Understanding human behavior requires assessing environmental statistics.
  • Accurate probability estimates for negative events are crucial for rational decision-making and preventative actions.
  • Investigating potential biases in probability estimation based on event severity is essential.

Purpose of the Study:

  • To determine if probability estimates for negative events are systematically biased by their severity.
  • To explore the relationship between the utility of an event and its perceived likelihood.
  • To test whether perceived likelihood is influenced by the negativity of an event's outcome.

Main Methods:

  • An experimental design with an unambiguous, objective representation of probability.
  • Participants judged the likelihood of controllable events under varying utility conditions.
  • Utilizing a minimal experimental context to isolate the effect of utility on probability estimation.

Main Results:

  • Participants judged controllable events as more likely to occur when their utility was extremely negative compared to neutral utility.
  • The study found a tendency for increased perceived likelihood with increased negative utility.
  • Objective probability representations were used to ensure clarity in the experimental setup.

Conclusions:

  • Probability estimates are not intrinsically biased by utilities, despite observed effects.
  • A decision-theoretic explanation involving loss function asymmetries supports the findings.
  • The perceived likelihood of negative events can be influenced by their severity, but this does not indicate an intrinsic bias in probability estimation.