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Related Concept Videos

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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Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

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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...
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Percentile01:18

Percentile

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A percentile indicates the relative standing of a data value when data are sorted into numerical order from smallest to largest. It represents the percentages of data values that are less than or equal to the pth percentile. For example, 15% of data values are less than or equal to the 15th percentile.
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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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Confidence interval for quantiles and percentiles.

Cristiano Ialongo1

  • 1Department of Human Physiology and Pharmacology, University of Rome Sapienza, Rome, Italy.

Biochemia Medica
|December 29, 2018
PubMed
Summary

This study compares methods for calculating confidence intervals (CI) for quantiles and percentiles in laboratory medicine. Parametric methods are most accurate, especially for extreme values, while non-parametric and bootstrap methods are suitable for central percentiles with unknown distributions.

Keywords:
biostatisticsconfidence intervalsextra-analytical phasestatistical methods

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Area of Science:

  • Laboratory Medicine
  • Biostatistics
  • Statistical Modeling

Background:

  • Quantiles and percentiles are crucial for describing data distributions and establishing reference intervals in laboratory medicine.
  • Estimating population parameters from sample data requires presenting these statistical measures with confidence intervals (CI).
  • Accurate CI estimation is vital for reliable performance specifications and quality indicators in clinical laboratories.

Purpose of the Study:

  • To evaluate and compare three distinct methods for estimating confidence intervals (CI) on quantiles and percentiles.
  • To assess the accuracy and applicability of parametric, nonparametric, and bootstrap approaches across different percentile ranges and sample sizes.
  • To provide guidance on selecting appropriate CI methods for laboratory medicine applications, particularly for reference intervals and performance specifications.

Main Methods:

  • Discussed three primary approaches for CI estimation: parametric, nonparametric, and resampling (bootstrap).
  • Conducted numerical simulations to compare the accuracy of these methods.
  • Evaluated performance for both extreme (2.5th, 97.5th) and central (25th, 50th, 75th) percentiles and their corresponding quantiles.

Main Results:

  • Parametric methods demonstrated superior accuracy irrespective of sample size when their assumptions align with the data distribution.
  • Both nonparametric and bootstrap methods proved effective for estimating CI of central percentiles.
  • Central percentile CI are particularly relevant for quality indicators in laboratory processes with unknown underlying distributions.

Conclusions:

  • Parametric methods are recommended for accurate CI estimation of quantiles and percentiles in laboratory medicine when distributional assumptions are met.
  • Nonparametric and bootstrap methods offer robust alternatives for central percentiles, especially in scenarios with unknown data distributions.
  • The choice of CI method impacts the reliability of reference intervals and performance specifications in laboratory diagnostics.