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Related Concept Videos

Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

7.2K
A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
A confidence interval for the mean is a range of values that provides an estimate of the population mean. As the...
7.2K
Prediction Intervals01:03

Prediction Intervals

2.2K
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

6.2K
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...
6.2K
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...
3.1K
Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

8.3K
To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
8.3K
Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

5.7K
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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Related Experiment Video

Updated: Jun 15, 2025

Measuring Delay Discounting in Humans Using an Adjusting Amount Task
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Measuring Delay Discounting in Humans Using an Adjusting Amount Task

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Introducing prediction intervals for sample means.

Molly E Contini1, Jeffrey R Spence1, David J Stanley1

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

Biochemia Medica
|August 22, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces prediction intervals, a valuable tool for statistical prediction, using basic statistical concepts like sampling error and standard deviation. It provides simple calculations and an R package for practical application in research and practice.

Keywords:
biostatisticseducationprediction intervalsresearch methodology

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Area of Science:

  • Statistics
  • Predictive Analytics

Background:

  • Descriptive statistics and statistical inference are widely understood by researchers and practitioners.
  • However, predictive methods beyond regression analysis often receive limited attention.

Purpose of the Study:

  • To introduce prediction intervals using fundamental statistical concepts.
  • To demonstrate the calculation of prediction intervals with simple hand examples and an R package.

Main Methods:

  • Utilized core statistical concepts including sampling error and standard deviation.
  • Provided step-by-step hand calculations for prediction interval estimation.
  • Referenced a user-friendly R package for computational ease.

Main Results:

  • Successfully demonstrated how to calculate prediction intervals from basic statistical principles.
  • Showcased the practical application of prediction intervals through a worked example.
  • Highlighted the utility of a specific R package for implementing these calculations.

Conclusions:

  • Prediction intervals can be readily understood and applied using foundational statistical knowledge.
  • The methods presented offer accessible tools for enhancing predictive capabilities in various fields.
  • Encourages broader adoption of prediction intervals in statistical practice and research.