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Related Concept Videos

Interval Level of Measurement00:55

Interval Level of Measurement

20.3K
For effective statistical analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using the interval scale are similar to ordinal level data because they have a definite arrangement. However, in the interval level of measurement, the differences between data values are meaningful even though the data does not have a starting point.
Temperature is measured using the interval scale. It is measurable data, and the difference between...
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Quartile01:15

Quartile

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Quartiles are numbers that separate the data into quarters. Quartiles may or may not be part of the data. To find the quartiles, first, find the median or second quartile. The first quartile, Q1, is the middle value of the lower half of the data, and the third quartile, Q3, is the middle value, or median, of the upper half of the data. To get the idea, consider the same data set:
1; 1; 2; 2; 4; 6; 6.8; 7.2; 8; 8.3; 9; 10; 10; 11.5
The median or second quartile is seven. The lower half of the...
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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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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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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 confidence...
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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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Updated: Apr 19, 2026

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
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Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

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Reference intervals data mining: no longer a probability paper method.

Alexander Katayev1, James K Fleming2, Dajie Luo2

  • 1From Laboratory Corporation of America Holdings, Elon, NC. katayea@labcorp.com.

American Journal of Clinical Pathology
|December 17, 2014
PubMed
Summary
This summary is machine-generated.

A new data-mining algorithm accurately calculates clinical laboratory reference intervals using existing data. This method provides a practical and reliable alternative to traditional direct sampling for establishing these vital health benchmarks.

Keywords:
EstimationInterpretationLaboratoryReference intervalResultTest

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Evaluation of a Point-of-Care Testing Analyzer for Measuring Peripheral Blood Leukocytes
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Area of Science:

  • Clinical Chemistry
  • Biostatistics
  • Laboratory Medicine

Background:

  • Establishing accurate reference intervals for clinical laboratory tests is crucial for disease diagnosis and monitoring.
  • Traditional methods for determining reference intervals often involve direct sampling, which can be resource-intensive.
  • Existing laboratory databases contain vast amounts of patient data that could be leveraged for reference interval calculation.

Purpose of the Study:

  • To present and evaluate a data-mining statistical algorithm for calculating clinical laboratory test reference intervals.
  • To demonstrate the algorithm's application using a diverse set of analytes and demographic groups.
  • To compare the algorithm-derived reference intervals with those from established peer-reviewed studies.

Main Methods:

  • A modified data-mining algorithm was applied to analyze test results stored in a laboratory database.
  • Reference intervals were calculated for eight analytes across different age and sex groups (11 total intervals).
  • Calculated intervals were statistically compared against published reference intervals derived from direct sampling.

Main Results:

  • The algorithm successfully calculated 11 distinct reference intervals.
  • Twenty-one out of 22 calculated reference interval limits showed no statistically significant difference compared to published studies.
  • The algorithm proved effective for analytes with predefined selection criteria, including method comparability and sufficient observations.

Conclusions:

  • The developed statistical algorithm is an accurate tool for calculating clinical laboratory reference intervals.
  • The data-mining approach offers a practical and efficient method for reference interval determination.
  • This algorithm can reliably utilize existing laboratory data, reducing the need for extensive direct sampling.