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Related Concept Videos

Prediction Intervals01:03

Prediction Intervals

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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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Range Rule of Thumb to Interpret Standard Deviation01:13

Range Rule of Thumb to Interpret Standard Deviation

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The range rule of thumb in statistics helps us calculate a dataset's minimum and maximum values with known standard deviation. This rule is based on the concept that 95% of all values in a dataset lie within two standard deviations from the mean.
For instance, the range rule of thumb can be used to find the tallest and the shortest student in a class, given the mean student height and standard deviation. If the mean student height is 1.6 m and the standard deviation, s is 0.05 m, the height...
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Interpreting R Charts01:22

Interpreting R Charts

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R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum...
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The R Chart01:02

The R Chart

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In statistical process control, control charts, particularly R charts, are instrumental in monitoring process variations and identifying non-random patterns that run charts might miss. R charts track the variability within process subgroups, which is crucial when standard deviation use is impractical or unknown process variations exist.
R charts are pivotal for pinpointing shifts in process variability. Stability is indicated when all data points remain within the defined upper and lower...
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Regression Toward the Mean01:52

Regression Toward the Mean

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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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Midrange01:07

Midrange

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A somewhat easy to compute quantitative estimate of a data set’s central tendency is its midrange, which is defined as the mean of the minimum and maximum values of an ordered data set.
Simply put, the midrange is half of the data set’s range. Similar to the mean, the midrange is sensitive to the extreme values and hence the prospective outliers. However, unlike the mean, the midrange is not sensitive to all the values of the data set that lie in the middle. Thus, it is prone to...
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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
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ReferenceRangeR: a novel tool designed to facilitate reference interval estimation and verification.

Gunnar Brandhorst1, Maike Voß1, Werner Wosniok2

  • 1Institute for Clinical Chemistry and Laboratory Medicine, University Medicine Oldenburg, Oldenburg, Germany.

Clinical Chemistry and Laboratory Medicine
|October 22, 2025
PubMed
Summary
This summary is machine-generated.

A new web-based application, ReferenceRangeR, simplifies the estimation and verification of laboratory reference intervals (RIs) using real-world data. This tool empowers laboratory professionals to meet regulatory standards without requiring specialized statistical or technical expertise.

Keywords:
estimationindirectreal-world-datareference intervaltool

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Area of Science:

  • Clinical Chemistry
  • Laboratory Medicine
  • Bioinformatics

Background:

  • Reference intervals (RIs) are crucial for accurate laboratory test interpretation.
  • Establishing and reviewing laboratory-specific RIs is a regulatory requirement.
  • Direct RI determination methods are often impractical, while indirect methods demand specialized expertise.

Purpose of the Study:

  • To develop a user-friendly web application for estimating and verifying reference intervals.
  • To provide an accessible tool for laboratories lacking statistical or technical expertise.
  • To facilitate compliance with current regulatory standards for reference interval establishment.

Main Methods:

  • Development of a web application using R Studio and the Shiny framework.
  • Integration of five indirect methods for reference interval estimation (refineR, TMC, TML, kosmic, reflimR).
  • Implementation of a Docker container for secure local deployment and a drift-detection algorithm for age-based stratification analysis.

Main Results:

  • The application accepts up to 200,000 laboratory test results via copy-paste.
  • It offers data-driven recommendations for sex-based stratification and analyzes the necessity of age-based stratification.
  • Results are visualized, and existing RIs can be verified against calculated intervals.

Conclusions:

  • ReferenceRangeR is an accessible, user-friendly tool for estimating and verifying RIs.
  • It removes the need for specialized statistical or technological expertise.
  • The tool supports laboratory professionals in adhering to regulatory requirements for RIs.