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Related Concept Videos

Probability in Statistics01:14

Probability in Statistics

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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...
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Confidence Coefficient01:24

Confidence Coefficient

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The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under...
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Uncertainty: Overview00:59

Uncertainty: Overview

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In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
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Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

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The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
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Statistical Analysis: Overview01:11

Statistical Analysis: Overview

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When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
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Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

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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...
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Related Experiment Video

Updated: May 21, 2025

Using the Threat Probability Task to Assess Anxiety and Fear During Uncertain and Certain Threat
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Combining probability with qualitative degree-of-certainty metrics in assessment.

Casey Helgeson1, Richard Bradley2, Brian Hill3

  • 1Earth and Environmental Systems Institute, Pennsylvania State University, 2217 Earth and Engineering Sciences Building, University Park, PA 16802 USA.

Climatic Change
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This study clarifies the relationship between likelihood and confidence metrics in Intergovernmental Panel on Climate Change (IPCC) reports. This improves mathematical consistency and usability for climate change assessments.

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Area of Science:

  • Climate science
  • Environmental policy
  • Risk assessment

Background:

  • The Intergovernmental Panel on Climate Change (IPCC) uses calibrated language to communicate scientific certainty.
  • A challenge in IPCC reports is the ambiguity between different metrics of certainty, such as likelihood and confidence.
  • This ambiguity affects mathematical consistency and downstream applications of IPCC findings.

Purpose of the Study:

  • To clarify the relationship between the likelihood and confidence metrics used in the IPCC's Fifth Assessment Report (2013).
  • To enhance mathematical consistency across multiple IPCC findings.
  • To improve the usability of IPCC uncertainty assessments for modeling and decision analysis.

Main Methods:

  • Analysis of the framework for assessing and communicating degrees of certainty in IPCC reports.
  • Examination of the specific metrics of likelihood and confidence from the Fifth Assessment Report.
  • Development of a proposal to clarify the relationship between these metrics.

Main Results:

  • The study proposes a clearer definition for the relationship between likelihood and confidence metrics.
  • The proposed framework aims to reduce ambiguity in IPCC uncertainty communication.
  • This clarification is expected to benefit mathematical consistency and downstream applications.

Conclusions:

  • Clarifying the relationship between likelihood and confidence metrics is crucial for robust climate change assessment.
  • The proposed approach supports current and future IPCC uncertainty assessment practices.
  • Improved clarity enhances the utility of IPCC findings for policy and decision-making.