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Related Concept Videos

Prediction Intervals01:03

Prediction Intervals

3.5K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Confidence Intervals01:21

Confidence Intervals

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An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A...
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Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

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A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
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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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Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

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Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
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Replication in Prokaryotes02:35

Replication in Prokaryotes

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Overview
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Prediction Interval: What to Expect When You're Expecting … A Replication.

Jeffrey R Spence1, David J Stanley1

  • 1Department of Psychology, University of Guelph, Guelph, Ontario, Canada.

Plos One
|September 20, 2016
PubMed
Summary
This summary is machine-generated.

Replication studies face challenges in determining success due to inherent study errors. This paper introduces prediction intervals to assess if replication results are due to chance sampling error, aiding interpretation.

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Area of Science:

  • Psychology
  • Statistics
  • Research Methodology

Background:

  • Interpreting replication studies is difficult due to inherent errors in individual studies.
  • Study results can deviate unpredictably from population effects because of various error sources.
  • Assessing replication success requires distinguishing genuine effects from random variation.

Purpose of the Study:

  • To derive methods for calculating prediction intervals for replication studies.
  • To provide a quantitative tool for assessing replication success.
  • To aid researchers in interpreting the consistency of replication results.

Main Methods:

  • Developed methods to compute prediction intervals for means, correlations, and d-values.
  • Prediction intervals account for sampling error based on original study effect size and sample sizes.
  • Methods are applicable even with unequal sample sizes across studies.

Main Results:

  • Introduced a calculable prediction interval based on objective study characteristics.
  • The prediction interval offers an a priori method to evaluate replication result differences.
  • Demonstrated how to assess if deviations are attributable to sampling error alone.

Conclusions:

  • Prediction intervals provide a robust framework for evaluating replication success.
  • The developed methods and open-source software facilitate easier assessment of replication consistency.
  • Enhances the reliability and interpretability of scientific replications.